Design Video Image Structure Analysis Platform

Posted on Edited on In

System Overview

The Video Image AI Structured Analysis Platform is a comprehensive solution designed to analyze video files, images, and real-time camera streams using advanced computer vision and machine learning algorithms. The platform extracts structured data about detected objects (persons, vehicles, bikes, motorbikes) and provides powerful search capabilities through multiple interfaces.

Key Capabilities

  • Real-time video stream processing from multiple cameras
  • Batch video file and image analysis
  • Object detection and attribute extraction
  • Distributed storage with similarity search
  • Scalable microservice architecture
  • Interactive web-based management interface

Architecture Overview


graph TB
subgraph "Client Layer"
    UI[Analysis Platform UI]
    API[REST APIs]
end

subgraph "Application Services"
    APS[Analysis Platform Service]
    TMS[Task Manager Service]
    SAS[Streaming Access Service]
    SAPS[Structure App Service]
    SSS[Storage And Search Service]
end

subgraph "Message Queue"
    KAFKA[Kafka Cluster]
end

subgraph "Storage Layer"
    REDIS[Redis Cache]
    ES[ElasticSearch]
    FASTDFS[FastDFS]
    VECTOR[Vector Database]
    ZK[Zookeeper]
end

subgraph "External"
    CAMERAS[IP Cameras]
    FILES[Video/Image Files]
end

UI --> APS
API --> APS
APS --> TMS
APS --> SSS
TMS --> ZK
SAS --> CAMERAS
SAS --> FILES
SAS --> SAPS
SAPS --> KAFKA
KAFKA --> SSS
SSS --> ES
SSS --> FASTDFS
SSS --> VECTOR
APS --> REDIS

Core Services Design

StreamingAccessService

The StreamingAccessService manages real-time video streams from distributed cameras and handles video file processing.

Key Features:

  • Multi-protocol camera support (RTSP, HTTP, WebRTC)
  • Video transcoding for format compatibility
  • Geographic camera distribution tracking
  • Load balancing across processing nodes

Implementation Example:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
@Service
@Component
public class StreamingAccessService {
    
    @Autowired
    private CameraRepository cameraRepository;
    
    @Autowired
    private VideoTranscodingService transcodingService;
    
    public void startCameraStream(String cameraId) {
        Camera camera = cameraRepository.findById(cameraId);
        
        StreamConfig config = StreamConfig.builder()
            .url(camera.getRtspUrl())
            .resolution(camera.getResolution())
            .frameRate(camera.getFrameRate())
            .build();
            
        StreamProcessor processor = new StreamProcessor(config);
        processor.onFrameReceived(frame -> {
            // Send frame to StructureAppService
            structureAppService.analyzeFrame(frame, camera.getLocation());
        });
        
        processor.start();
    }
    
    public List<CameraInfo> getCamerasInRegion(GeoLocation center, double radius) {
        return cameraRepository.findWithinRadius(center, radius)
            .stream()
            .map(this::toCameraInfo)
            .collect(Collectors.toList());
    }
}

Interview Question: How would you handle camera connection failures and ensure high availability?

Answer: Implement circuit breaker patterns, retry mechanisms with exponential backoff, health check endpoints, and failover to backup cameras. Use connection pooling and maintain camera status in Redis for quick status checks.

StructureAppService

This service performs the core AI analysis using computer vision models deployed on GPU-enabled infrastructure.

Object Detection Pipeline:


flowchart LR
A[Input Frame] --> B[Preprocessing]
B --> C[Object Detection]
C --> D[Attribute Extraction]
D --> E[Structured Output]
E --> F[Kafka Publisher]

subgraph "AI Models"
    G[YOLO V8 Detection]
    H[Age/Gender Classification]
    I[Vehicle Recognition]
    J[Attribute Extraction]
end

C --> G
D --> H
D --> I
D --> J

Object Analysis Specifications:

Person Attributes:

  • Age estimation (age ranges: 0-12, 13-17, 18-30, 31-50, 51-70, 70+)
  • Gender classification (male, female, unknown)
  • Height estimation using reference objects
  • Clothing color detection (top, bottom)
  • Body size estimation (small, medium, large)
  • Pose estimation for activity recognition

Vehicle Attributes:

  • License plate recognition using OCR
  • Vehicle type classification (sedan, SUV, truck, bus)
  • Color detection using color histograms
  • Brand recognition using CNN models
  • Seatbelt detection for driver safety compliance

Service Implementation:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
@Service
public class StructureAppService {
    
    @Autowired
    private ObjectDetectionModel objectDetectionModel;
    
    @Autowired
    private AttributeExtractionService attributeService;
    
    @Autowired
    private KafkaProducer<String, AnalysisResult> kafkaProducer;
    
    public void analyzeFrame(VideoFrame frame, GeoLocation location) {
        try {
            // Preprocess frame
            ProcessedFrame processed = preprocessFrame(frame);
            
            // Detect objects
            List<DetectedObject> objects = objectDetectionModel.detect(processed);
            
            // Extract attributes for each object
            List<StructuredObject> structuredObjects = objects.stream()
                .map(obj -> extractAttributes(obj, processed))
                .collect(Collectors.toList());
            
            // Create analysis result
            AnalysisResult result = AnalysisResult.builder()
                .timestamp(Instant.now())
                .location(location)
                .objects(structuredObjects)
                .frameId(frame.getId())
                .build();
            
            // Send to Kafka
            kafkaProducer.send("analysis-results", result);
            
        } catch (Exception e) {
            log.error("Analysis failed for frame: {}", frame.getId(), e);
            // Send to dead letter queue
            handleAnalysisFailure(frame, e);
        }
    }
    
    private StructuredObject extractAttributes(DetectedObject object, ProcessedFrame frame) {
        switch (object.getType()) {
            case PERSON:
                return extractPersonAttributes(object, frame);
            case VEHICLE:
                return extractVehicleAttributes(object, frame);
            case BIKE:
            case MOTORBIKE:
                return extractBikeAttributes(object, frame);
            default:
                return createBasicStructuredObject(object);
        }
    }
    
    private PersonObject extractPersonAttributes(DetectedObject object, ProcessedFrame frame) {
        Rectangle bbox = object.getBoundingBox();
        BufferedImage personImage = frame.getSubImage(bbox);
        
        return PersonObject.builder()
            .id(UUID.randomUUID().toString())
            .age(ageClassifier.predict(personImage))
            .gender(genderClassifier.predict(personImage))
            .height(estimateHeight(bbox, frame.getDepthInfo()))
            .clothingColor(colorExtractor.extractClothingColor(personImage))
            .trouserColor(colorExtractor.extractTrouserColor(personImage))
            .size(estimateBodySize(bbox))
            .confidence(object.getConfidence())
            .bbox(bbox)
            .build();
    }
}

GPU Resource Management:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
@Component
public class GPUResourceManager {
    
    private final Queue<GPUTask> taskQueue = new ConcurrentLinkedQueue<>();
    private final List<GPUWorker> workers;
    
    @PostConstruct
    public void initializeWorkers() {
        int gpuCount = getAvailableGPUs();
        for (int i = 0; i < gpuCount; i++) {
            workers.add(new GPUWorker(i, taskQueue));
        }
    }
    
    public CompletableFuture<AnalysisResult> submitTask(VideoFrame frame) {
        GPUTask task = new GPUTask(frame);
        taskQueue.offer(task);
        return task.getFuture();
    }
}

Interview Question: How do you optimize GPU utilization for real-time video analysis?

Answer: Use batch processing to maximize GPU throughput, implement dynamic batching based on queue depth, utilize GPU memory pooling, and employ model quantization. Monitor GPU metrics and auto-scale workers based on load.

StorageAndSearchService

Manages distributed storage across ElasticSearch, FastDFS, and vector databases.

ElasticSearch Index Mappings:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
{
  "person_index": {
    "mappings": {
      "properties": {
        "id": {"type": "keyword"},
        "timestamp": {"type": "date"},
        "location": {
          "type": "geo_point"
        },
        "age": {"type": "integer"},
        "gender": {"type": "keyword"},
        "height": {"type": "float"},
        "clothing_color": {"type": "keyword"},
        "trouser_color": {"type": "keyword"},
        "size": {"type": "keyword"},
        "confidence": {"type": "float"},
        "image_path": {"type": "keyword"},
        "vector_id": {"type": "keyword"},
        "bbox": {
          "properties": {
            "x": {"type": "integer"},
            "y": {"type": "integer"},
            "width": {"type": "integer"},
            "height": {"type": "integer"}
          }
        }
      }
    }
  },
  "vehicle_index": {
    "mappings": {
      "properties": {
        "id": {"type": "keyword"},
        "timestamp": {"type": "date"},
        "location": {"type": "geo_point"},
        "plate_number": {"type": "keyword"},
        "color": {"type": "keyword"},
        "brand": {"type": "keyword"},
        "model": {"type": "text"},
        "driver_seatbelt": {"type": "boolean"},
        "vehicle_type": {"type": "keyword"},
        "confidence": {"type": "float"},
        "image_path": {"type": "keyword"},
        "vector_id": {"type": "keyword"}
      }
    }
  },
  "bike_index": {
    "mappings": {
      "properties": {
        "id": {"type": "keyword"},
        "timestamp": {"type": "date"},
        "location": {"type": "geo_point"},
        "type": {"type": "keyword"},
        "color": {"type": "keyword"},
        "rider_helmet": {"type": "boolean"},
        "rider_count": {"type": "integer"},
        "confidence": {"type": "float"},
        "image_path": {"type": "keyword"},
        "vector_id": {"type": "keyword"}
      }
    }
  }
}

Service Implementation:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
@Service
public class StorageAndSearchService {
    
    @Autowired
    private ElasticsearchClient elasticsearchClient;
    
    @Autowired
    private FastDFSClient fastDFSClient;
    
    @Autowired
    private VectorDatabaseClient vectorClient;
    
    @KafkaListener(topics = "analysis-results")
    public void processAnalysisResult(AnalysisResult result) {
        result.getObjects().forEach(this::storeObject);
    }
    
    private void storeObject(StructuredObject object) {
        try {
            // Store image in FastDFS
            String imagePath = storeImage(object.getImage());
            
            // Store vector representation
            String vectorId = storeVector(object.getImageVector());
            
            // Store structured data in ElasticSearch
            storeInElasticSearch(object, imagePath, vectorId);
            
        } catch (Exception e) {
            log.error("Failed to store object: {}", object.getId(), e);
        }
    }
    
    private String storeImage(BufferedImage image) {
        byte[] imageBytes = convertToBytes(image);
        return fastDFSClient.uploadFile(imageBytes, "jpg");
    }
    
    private String storeVector(float[] vector) {
        return vectorClient.store(vector, Map.of(
            "timestamp", Instant.now().toString(),
            "type", "image_embedding"
        ));
    }
    
    public SearchResult<PersonObject> searchPersons(PersonSearchQuery query) {
        BoolQuery.Builder boolQuery = QueryBuilders.bool();
        
        if (query.getAge() != null) {
            boolQuery.must(QueryBuilders.range(r -> r
                .field("age")
                .gte(JsonData.of(query.getAge() - 5))
                .lte(JsonData.of(query.getAge() + 5))));
        }
        
        if (query.getGender() != null) {
            boolQuery.must(QueryBuilders.term(t -> t
                .field("gender")
                .value(query.getGender())));
        }
        
        if (query.getLocation() != null) {
            boolQuery.must(QueryBuilders.geoDistance(g -> g
                .field("location")
                .location(l -> l.latlon(query.getLocation()))
                .distance(query.getRadius() + "km")));
        }
        
        SearchRequest request = SearchRequest.of(s -> s
            .index("person_index")
            .query(boolQuery.build()._toQuery())
            .size(query.getLimit())
            .from(query.getOffset()));
            
        SearchResponse<PersonObject> response = elasticsearchClient.search(request, PersonObject.class);
        
        return convertToSearchResult(response);
    }
    
    public List<SimilarObject> findSimilarImages(BufferedImage queryImage, int limit) {
        float[] queryVector = imageEncoder.encode(queryImage);
        
        return vectorClient.similaritySearch(queryVector, limit)
            .stream()
            .map(this::enrichWithMetadata)
            .collect(Collectors.toList());
    }
}

Interview Question: How do you ensure data consistency across multiple storage systems?

Answer: Implement saga pattern for distributed transactions, use event sourcing with Kafka for eventual consistency, implement compensation actions for rollback scenarios, and maintain idempotency keys for retry safety.

TaskManagerService

Coordinates task execution across distributed nodes using Zookeeper for coordination.

Task Management:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
@Service
public class TaskManagerService {
    
    @Autowired
    private CuratorFramework zookeeperClient;
    
    @Autowired
    private NodeResourceMonitor resourceMonitor;
    
    private final String TASKS_PATH = "/video-analysis/tasks";
    private final String NODES_PATH = "/video-analysis/nodes";
    
    public String createTask(AnalysisTask task) {
        try {
            String taskId = UUID.randomUUID().toString();
            String taskPath = TASKS_PATH + "/" + taskId;
            
            TaskInfo taskInfo = TaskInfo.builder()
                .id(taskId)
                .type(task.getType())
                .input(task.getInput())
                .location(task.getLocation())
                .status(TaskStatus.PENDING)
                .createdAt(Instant.now())
                .build();
            
            zookeeperClient.create()
                .creatingParentsIfNeeded()
                .withMode(CreateMode.PERSISTENT)
                .forPath(taskPath, SerializationUtils.serialize(taskInfo));
            
            scheduleTask(taskId);
            return taskId;
            
        } catch (Exception e) {
            throw new TaskCreationException("Failed to create task", e);
        }
    }
    
    private void scheduleTask(String taskId) {
        // Find best node based on resource availability
        Optional<NodeInfo> bestNode = findBestAvailableNode();
        
        if (bestNode.isPresent()) {
            // Create a distributed lock for task assignment
            InterProcessMutex lock = new InterProcessMutex(zookeeperClient, 
                "/tasks/locks/" + task.getId());
            try {
                lock.acquire();
                assignTaskToNode(taskId, bestNode.get());
            } finally {
                lock.release();
            }
        } else {
            // Queue task for later execution
            queueTask(taskId);
        }
    }
    
    private Optional<NodeInfo> findBestAvailableNode() {
        try {
            List<String> nodes = zookeeperClient.getChildren().forPath(NODES_PATH);
            
            return nodes.stream()
                .map(this::getNodeInfo)
                .filter(Objects::nonNull)
                .filter(node -> node.getGpuUsage() < 0.8) // Less than 80% GPU usage
                .max(Comparator.comparing(node -> 
                    node.getAvailableGpuMemory() + node.getAvailableCpuCores()));
                    
        } catch (Exception e) {
            log.error("Failed to find available node", e);
            return Optional.empty();
        }
    }
    
    @EventListener
    public void handleTaskCompletion(TaskCompletedEvent event) {
        updateTaskStatus(event.getTaskId(), TaskStatus.COMPLETED);
        releaseNodeResources(event.getNodeId());
        scheduleQueuedTasks();
    }
}

Node Resource Monitoring:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
@Component
public class NodeResourceMonitor {
    
    @Scheduled(fixedRate = 5000) // Every 5 seconds
    public void updateNodeResources() {
        NodeInfo nodeInfo = getCurrentNodeInfo();
        
        try {
            String nodePath = NODES_PATH + "/" + getNodeId();
            
            if (zookeeperClient.checkExists().forPath(nodePath) == null) {
                zookeeperClient.create()
                    .creatingParentsIfNeeded()
                    .withMode(CreateMode.EPHEMERAL)
                    .forPath(nodePath, SerializationUtils.serialize(nodeInfo));
            } else {
                zookeeperClient.setData()
                    .forPath(nodePath, SerializationUtils.serialize(nodeInfo));
            }
            
        } catch (Exception e) {
            log.error("Failed to update node resources", e);
        }
    }
    
    private NodeInfo getCurrentNodeInfo() {
        return NodeInfo.builder()
            .id(getNodeId())
            .cpuUsage(getCpuUsage())
            .memoryUsage(getMemoryUsage())
            .gpuUsage(getGpuUsage())
            .availableGpuMemory(getAvailableGpuMemory())
            .availableCpuCores(getAvailableCpuCores())
            .lastHeartbeat(Instant.now())
            .build();
    }
}

AnalysisPlatformService

Provides REST APIs for the frontend application with comprehensive caching strategies.

API Design:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
@RestController
@RequestMapping("/api/v1")
@Slf4j
public class AnalysisPlatformController {
    
    @Autowired
    private TaskManagerService taskManagerService;
    
    @Autowired
    private StorageAndSearchService searchService;
    
    @Autowired
    private RedisTemplate<String, Object> redisTemplate;
    
    // Task Management APIs
    @PostMapping("/tasks")
    public ResponseEntity<TaskResponse> createTask(@RequestBody CreateTaskRequest request) {
        AnalysisTask task = convertToTask(request);
        String taskId = taskManagerService.createTask(task);
        
        return ResponseEntity.ok(TaskResponse.builder()
            .taskId(taskId)
            .status("CREATED")
            .build());
    }
    
    @GetMapping("/tasks/{taskId}")
    @Cacheable(value = "tasks", key = "#taskId")
    public ResponseEntity<TaskInfo> getTask(@PathVariable String taskId) {
        TaskInfo task = taskManagerService.getTask(taskId);
        return ResponseEntity.ok(task);
    }
    
    @GetMapping("/tasks")
    public ResponseEntity<PagedResult<TaskInfo>> getTasks(
            @RequestParam(defaultValue = "0") int page,
            @RequestParam(defaultValue = "20") int size,
            @RequestParam(required = false) String status) {
        
        TaskQuery query = TaskQuery.builder()
            .page(page)
            .size(size)
            .status(status)
            .build();
            
        PagedResult<TaskInfo> tasks = taskManagerService.getTasks(query);
        return ResponseEntity.ok(tasks);
    }
    
    // Camera Management APIs
    @PostMapping("/cameras")
    public ResponseEntity<CameraInfo> createCamera(@RequestBody CreateCameraRequest request) {
        CameraInfo camera = cameraService.createCamera(request);
        return ResponseEntity.ok(camera);
    }
    
    @GetMapping("/cameras/map")
    @Cacheable(value = "camera-map", key = "#center + '-' + #zoom")
    public ResponseEntity<List<CameraMapInfo>> getCamerasForMap(
            @RequestParam String center,
            @RequestParam int zoom) {
        
        GeoLocation centerPoint = parseGeoLocation(center);
        double radius = calculateRadius(zoom);
        
        List<CameraMapInfo> cameras = cameraService.getCamerasInRegion(centerPoint, radius);
        return ResponseEntity.ok(cameras);
    }
    
    // Search APIs
    @PostMapping("/search/persons")
    public ResponseEntity<SearchResult<PersonObject>> searchPersons(
            @RequestBody PersonSearchQuery query) {
        
        String cacheKey = generateCacheKey("person-search", query);
        SearchResult<PersonObject> cached = getCachedResult(cacheKey);
        
        if (cached != null) {
            return ResponseEntity.ok(cached);
        }
        
        SearchResult<PersonObject> result = searchService.searchPersons(query);
        cacheResult(cacheKey, result, Duration.ofMinutes(5));
        
        return ResponseEntity.ok(result);
    }
    
    @PostMapping("/search/vehicles")
    public ResponseEntity<SearchResult<VehicleObject>> searchVehicles(
            @RequestBody VehicleSearchQuery query) {
        
        SearchResult<VehicleObject> result = searchService.searchVehicles(query);
        return ResponseEntity.ok(result);
    }
    
    @PostMapping("/search/similarity")
    public ResponseEntity<List<SimilarObject>> searchSimilar(
            @RequestParam("image") MultipartFile imageFile,
            @RequestParam(defaultValue = "10") int limit) {
        
        try {
            BufferedImage image = ImageIO.read(imageFile.getInputStream());
            List<SimilarObject> similar = searchService.findSimilarImages(image, limit);
            return ResponseEntity.ok(similar);
            
        } catch (Exception e) {
            return ResponseEntity.badRequest().build();
        }
    }
    
    // Statistics APIs
    @GetMapping("/stats/objects")
    @Cacheable(value = "object-stats", key = "#timeRange")
    public ResponseEntity<ObjectStatistics> getObjectStatistics(
            @RequestParam String timeRange) {
        
        ObjectStatistics stats = analyticsService.getObjectStatistics(timeRange);
        return ResponseEntity.ok(stats);
    }
}

Complete API Specification:

Endpoint Method Description Cache TTL
/api/v1/tasks POST Create analysis task -
/api/v1/tasks/{id} GET Get task details 1 min
/api/v1/tasks GET List tasks with pagination 30 sec
/api/v1/cameras POST Register new camera -
/api/v1/cameras/{id} PUT Update camera config -
/api/v1/cameras/map GET Get cameras for map view 5 min
/api/v1/search/persons POST Search persons by attributes 5 min
/api/v1/search/vehicles POST Search vehicles by attributes 5 min
/api/v1/search/similarity POST Image similarity search 1 min
/api/v1/stats/objects GET Object detection statistics 10 min

Interview Question: How do you handle API rate limiting and prevent abuse?

Answer: Implement token bucket algorithm with Redis, use sliding window counters, apply different limits per user tier, implement circuit breakers for downstream services, and use API gateways for centralized rate limiting.

Microservice Architecture with Spring Cloud Alibaba

Service Discovery and Configuration:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
# application.yml for each service
spring:
  application:
    name: analysis-platform-service
  cloud:
    nacos:
      discovery:
        server-addr: ${NACOS_SERVER:localhost:8848}
        namespace: ${NACOS_NAMESPACE:dev}
      config:
        server-addr: ${NACOS_SERVER:localhost:8848}
        namespace: ${NACOS_NAMESPACE:dev}
        file-extension: yaml
    sentinel:
      transport:
        dashboard: ${SENTINEL_DASHBOARD:localhost:8080}
  profiles:
    active: ${SPRING_PROFILES_ACTIVE:dev}

Circuit Breaker Configuration:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
@Component
public class VideoAnalysisServiceFallback implements VideoAnalysisService {
    
    @Override
    public AnalysisResult analyzeVideo(VideoRequest request) {
        return AnalysisResult.builder()
            .status("FALLBACK")
            .message("Video analysis service temporarily unavailable")
            .build();
    }
}

@FeignClient(
    name = "structure-app-service", 
    fallback = VideoAnalysisServiceFallback.class
)
public interface VideoAnalysisService {
    
    @PostMapping("/analyze")
    AnalysisResult analyzeVideo(@RequestBody VideoRequest request);
}

Scalability and Performance Optimization

Horizontal Scaling Strategy:


graph LR
subgraph "Load Balancer"
    LB[Nginx/HAProxy]
end

subgraph "API Gateway"
    GW1[Gateway 1]
    GW2[Gateway 2]
    GW3[Gateway 3]
end

subgraph "Analysis Platform Services"
    APS1[Service 1]
    APS2[Service 2]
    APS3[Service 3]
end

subgraph "Structure App Services"
    SAS1[GPU Node 1]
    SAS2[GPU Node 2]
    SAS3[GPU Node 3]
end

LB --> GW1
LB --> GW2
LB --> GW3

GW1 --> APS1
GW2 --> APS2
GW3 --> APS3

APS1 --> SAS1
APS2 --> SAS2
APS3 --> SAS3

Auto-scaling Configuration:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
# Kubernetes HPA configuration
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: analysis-platform-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: analysis-platform-service
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

Caching Strategy:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
@Configuration
@EnableCaching
public class CacheConfiguration {
    
    @Bean
    public CacheManager cacheManager() {
        RedisCacheManager.Builder builder = RedisCacheManager
            .RedisCacheManagerBuilder
            .fromConnectionFactory(redisConnectionFactory())
            .cacheDefaults(cacheConfiguration());
            
        return builder.build();
    }
    
    private RedisCacheConfiguration cacheConfiguration() {
        return RedisCacheConfiguration.defaultCacheConfig()
            .entryTtl(Duration.ofMinutes(10))
            .serializeKeysWith(RedisSerializationContext.SerializationPair
                .fromSerializer(new StringRedisSerializer()))
            .serializeValuesWith(RedisSerializationContext.SerializationPair
                .fromSerializer(new GenericJackson2JsonRedisSerializer()));
    }
}

Frontend Implementation

Camera Map Integration:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
// React component for camera map
import React, { useState, useEffect } from 'react';
import { MapContainer, TileLayer, Marker, Popup } from 'react-leaflet';

const CameraMap = () => {
  const [cameras, setCameras] = useState([]);
  const [loading, setLoading] = useState(true);

  useEffect(() => {
    fetchCameras();
  }, []);

  const fetchCameras = async () => {
    try {
      const response = await fetch('/api/v1/cameras/map?center=39.9042,116.4074&zoom=10');
      const data = await response.json();
      setCameras(data);
    } catch (error) {
      console.error('Failed to fetch cameras:', error);
    } finally {
      setLoading(false);
    }
  };

  return (
    <MapContainer center={[39.9042, 116.4074]} zoom={10} style={{ height: '600px' }}>
      <TileLayer
        url="https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png"
        attribution='&copy; OpenStreetMap contributors'
      />
      {cameras.map(camera => (
        <Marker key={camera.id} position={[camera.latitude, camera.longitude]}>
          <Popup>
            <div>
              <h4>{camera.name}</h4>
              <p>Status: {camera.status}</p>
              <p>Location: {camera.address}</p>
              <button onClick={() => startAnalysis(camera.id)}>
                Start Analysis
              </button>
            </div>
          </Popup>
        </Marker>
      ))}
    </MapContainer>
  );
};

Search Interface:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
const SearchInterface = () => {
  const [searchType, setSearchType] = useState('person');
  const [searchQuery, setSearchQuery] = useState({});
  const [results, setResults] = useState([]);

  const handleSearch = async () => {
    const endpoint = `/api/v1/search/${searchType}s`;
    const response = await fetch(endpoint, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify(searchQuery)
    });
    
    const data = await response.json();
    setResults(data.items);
  };

  return (
    <div className="search-interface">
      <div className="search-controls">
        <select value={searchType} onChange={(e) => setSearchType(e.target.value)}>
          <option value="person">Person</option>
          <option value="vehicle">Vehicle</option>
          <option value="bike">Bike</option>
        </select>
        
        {searchType === 'person' && (
          <PersonSearchForm 
            query={searchQuery} 
            onChange={setSearchQuery} 
          />
        )}
        
        <button onClick={handleSearch}>Search</button>
      </div>
      
      <SearchResults results={results} />
    </div>
  );
};

Docker Deployment Configuration

Multi-stage Dockerfile:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
# Analysis Platform Service
FROM openjdk:17-jdk-slim as builder
WORKDIR /app
COPY pom.xml .
COPY src ./src
RUN mvn clean package -DskipTests

FROM openjdk:17-jre-slim
RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY --from=builder /app/target/analysis-platform-service.jar app.jar

EXPOSE 8080
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
    CMD curl -f http://localhost:8080/actuator/health || exit 1

ENTRYPOINT ["java", "-jar", "app.jar"]

Structure App Service (GPU-enabled):

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
# Structure App Service with GPU support
FROM nvidia/cuda:11.8-devel-ubuntu20.04 as base

# Install Python and dependencies
RUN apt-get update && apt-get install -y \
    python3.9 \
    python3-pip \
    python3-dev \
    build-essential \
    cmake \
    libopencv-dev \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    libgomp1 \
    && rm -rf /var/lib/apt/lists/*

# Install Python packages
COPY requirements.txt .
RUN pip3 install --no-cache-dir -r requirements.txt

# Install PyTorch with CUDA support
RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# Copy application code
WORKDIR /app
COPY . .

# Download pre-trained models
RUN python3 download_models.py

EXPOSE 8081
CMD ["python3", "app.py"]

Docker Compose for Development:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
version: '3.8'

services:
  zookeeper:
    image: confluentinc/cp-zookeeper:7.4.0
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000
    ports:
      - "2181:2181"
    volumes:
      - zookeeper-data:/var/lib/zookeeper/data

  kafka:
    image: confluentinc/cp-kafka:7.4.0
    depends_on:
      - zookeeper
    ports:
      - "9092:9092"
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
    volumes:
      - kafka-data:/var/lib/kafka/data

  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.9.0
    environment:
      - discovery.type=single-node
      - "ES_JAVA_OPTS=-Xms2g -Xmx2g"
      - xpack.security.enabled=false
    ports:
      - "9200:9200"
    volumes:
      - elasticsearch-data:/usr/share/elasticsearch/data

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - redis-data:/data
    command: redis-server --appendonly yes

  fastdfs-tracker:
    image: delron/fastdfs
    ports:
      - "22122:22122"
    command: tracker
    volumes:
      - fastdfs-tracker:/var/fdfs

  fastdfs-storage:
    image: delron/fastdfs
    ports:
      - "8888:8888"
    command: storage
    environment:
      TRACKER_SERVER: fastdfs-tracker:22122
    volumes:
      - fastdfs-storage:/var/fdfs
    depends_on:
      - fastdfs-tracker

  vector-db:
    image: milvusdb/milvus:v2.3.0
    ports:
      - "19530:19530"
    environment:
      ETCD_ENDPOINTS: etcd:2379
      MINIO_ADDRESS: minio:9000
    depends_on:
      - etcd
      - minio

  analysis-platform-service:
    build: ./analysis-platform-service
    ports:
      - "8080:8080"
    environment:
      SPRING_PROFILES_ACTIVE: docker
      KAFKA_BOOTSTRAP_SERVERS: kafka:9092
      REDIS_HOST: redis
      ELASTICSEARCH_HOST: elasticsearch
      ZOOKEEPER_CONNECT: zookeeper:2181
    depends_on:
      - kafka
      - redis
      - elasticsearch
      - zookeeper

  structure-app-service:
    build: ./structure-app-service
    ports:
      - "8081:8081"
    environment:
      KAFKA_BOOTSTRAP_SERVERS: kafka:9092
      REDIS_HOST: redis
    depends_on:
      - kafka
      - redis
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

volumes:
  zookeeper-data:
  kafka-data:
  elasticsearch-data:
  redis-data:
  fastdfs-tracker:
  fastdfs-storage:

Performance Optimization Strategies

Database Query Optimization:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
@Repository
public class OptimizedPersonRepository {
    
    @Autowired
    private ElasticsearchClient client;
    
    public SearchResult<PersonObject> searchWithAggregations(PersonSearchQuery query) {
        // Use composite aggregations for better performance
        SearchRequest request = SearchRequest.of(s -> s
            .index("person_index")
            .query(buildQuery(query))
            .aggregations("age_groups", a -> a
                .histogram(h -> h
                    .field("age")
                    .interval(10.0)))
            .aggregations("gender_distribution", a -> a
                .terms(t -> t
                    .field("gender")
                    .size(10)))
            .aggregations("location_clusters", a -> a
                .geohashGrid(g -> g
                    .field("location")
                    .precision(5)))
            .size(query.getLimit())
            .from(query.getOffset())
            .sort(SortOptions.of(so -> so
                .field(f -> f
                    .field("timestamp")
                    .order(SortOrder.Desc)))));
        
        SearchResponse<PersonObject> response = client.search(request, PersonObject.class);
        return convertToSearchResult(response);
    }
    
    // Batch processing for bulk operations
    @Async
    public CompletableFuture<Void> bulkIndexPersons(List<PersonObject> persons) {
        BulkRequest.Builder bulkBuilder = new BulkRequest.Builder();
        
        for (PersonObject person : persons) {
            bulkBuilder.operations(op -> op
                .index(idx -> idx
                    .index("person_index")
                    .id(person.getId())
                    .document(person)));
        }
        
        BulkResponse response = client.bulk(bulkBuilder.build());
        
        if (response.errors()) {
            log.error("Bulk indexing errors: {}", response.items().size());
        }
        
        return CompletableFuture.completedFuture(null);
    }
}

Memory and CPU Optimization:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
@Service
public class OptimizedImageProcessor {
    
    private final ExecutorService imageProcessingPool;
    private final ObjectPool<BufferedImage> imagePool;
    
    public OptimizedImageProcessor() {
        // Create bounded thread pool for image processing
        this.imageProcessingPool = new ThreadPoolExecutor(
            4, // core threads
            Runtime.getRuntime().availableProcessors() * 2, // max threads
            60L, TimeUnit.SECONDS,
            new ArrayBlockingQueue<>(1000),
            new ThreadFactoryBuilder()
                .setNameFormat("image-processor-%d")
                .setDaemon(true)
                .build(),
            new ThreadPoolExecutor.CallerRunsPolicy()
        );
        
        // Object pool for reusing BufferedImage objects
        this.imagePool = new GenericObjectPool<>(new BufferedImageFactory());
    }
    
    public CompletableFuture<ProcessedImage> processImageAsync(byte[] imageData) {
        return CompletableFuture.supplyAsync(() -> {
            BufferedImage image = null;
            try {
                image = imagePool.borrowObject();
                
                // Resize image to standard dimensions
                BufferedImage resized = resizeImage(imageData, 640, 480);
                
                // Apply optimizations
                return ProcessedImage.builder()
                    .image(resized)
                    .metadata(extractMetadata(resized))
                    .build();
                    
            } catch (Exception e) {
                throw new ImageProcessingException("Failed to process image", e);
            } finally {
                if (image != null) {
                    try {
                        imagePool.returnObject(image);
                    } catch (Exception e) {
                        log.warn("Failed to return image to pool", e);
                    }
                }
            }
        }, imageProcessingPool);
    }
    
    private BufferedImage resizeImage(byte[] imageData, int width, int height) {
        try (InputStream is = new ByteArrayInputStream(imageData)) {
            BufferedImage original = ImageIO.read(is);
            
            // Use high-quality scaling
            BufferedImage resized = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
            Graphics2D g2d = resized.createGraphics();
            
            g2d.setRenderingHint(RenderingHints.KEY_INTERPOLATION, 
                RenderingHints.VALUE_INTERPOLATION_BILINEAR);
            g2d.setRenderingHint(RenderingHints.KEY_RENDERING, 
                RenderingHints.VALUE_RENDER_QUALITY);
            
            g2d.drawImage(original, 0, 0, width, height, null);
            g2d.dispose();
            
            return resized;
            
        } catch (IOException e) {
            throw new ImageProcessingException("Failed to resize image", e);
        }
    }
}

Monitoring and Observability

Comprehensive Monitoring Setup:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
@Component
public class SystemMetrics {
    
    private final MeterRegistry meterRegistry;
    private final Counter videoAnalysisCounter;
    private final Timer analysisTimer;
    private final Gauge activeTasksGauge;
    
    public SystemMetrics(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
        this.videoAnalysisCounter = Counter.builder("video.analysis.total")
            .description("Total number of video analysis requests")
            .tag("service", "structure-app")
            .register(meterRegistry);
            
        this.analysisTimer = Timer.builder("video.analysis.duration")
            .description("Video analysis processing time")
            .register(meterRegistry);
            
        this.activeTasksGauge = Gauge.builder("tasks.active")
            .description("Number of active analysis tasks")
            .register(meterRegistry, this, SystemMetrics::getActiveTaskCount);
    }
    
    public void recordAnalysis(String objectType, Duration duration, String result) {
        videoAnalysisCounter.increment(
            Tags.of(
                "object.type", objectType,
                "result", result
            )
        );
        
        analysisTimer.record(duration);
    }
    
    private double getActiveTaskCount() {
        return taskManagerService.getActiveTaskCount();
    }
}

Distributed Tracing:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
@RestController
public class TracedAnalysisController {
    
    @Autowired
    private Tracer tracer;
    
    @PostMapping("/analyze")
    public ResponseEntity<AnalysisResult> analyzeVideo(@RequestBody VideoRequest request) {
        Span span = tracer.nextSpan()
            .name("video-analysis")
            .tag("video.size", String.valueOf(request.getSize()))
            .tag("video.format", request.getFormat())
            .start();
            
        try (Tracer.SpanInScope ws = tracer.withSpanInScope(span)) {
            AnalysisResult result = performAnalysis(request);
            
            span.tag("objects.detected", String.valueOf(result.getObjectCount()));
            span.tag("analysis.status", result.getStatus());
            
            return ResponseEntity.ok(result);
            
        } catch (Exception e) {
            span.tag("error", e.getMessage());
            throw e;
        } finally {
            span.end();
        }
    }
}

Health Checks and Alerting:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
@Component
public class CustomHealthIndicator implements HealthIndicator {
    
    @Autowired
    private GPUResourceMonitor gpuMonitor;
    
    @Autowired
    private KafkaHealthIndicator kafkaHealth;
    
    @Override
    public Health health() {
        Health.Builder builder = new Health.Builder();
        
        // Check GPU availability
        if (gpuMonitor.getAvailableGPUs() == 0) {
            return builder.down()
                .withDetail("gpu", "No GPUs available")
                .build();
        }
        
        // Check Kafka connectivity
        if (!kafkaHealth.isHealthy()) {
            return builder.down()
                .withDetail("kafka", "Kafka connection failed")
                .build();
        }
        
        // Check memory usage
        double memoryUsage = getMemoryUsage();
        if (memoryUsage > 0.9) {
            return builder.down()
                .withDetail("memory", "Memory usage critical: " + memoryUsage)
                .build();
        }
        
        return builder.up()
            .withDetail("gpu.count", gpuMonitor.getAvailableGPUs())
            .withDetail("memory.usage", memoryUsage)
            .withDetail("kafka.status", "healthy")
            .build();
    }
}

Security Implementation

Authentication and Authorization:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
@Configuration
@EnableWebSecurity
public class SecurityConfig {
    
    @Bean
    public SecurityFilterChain filterChain(HttpSecurity http) throws Exception {
        return http
            .csrf().disable()
            .sessionManagement().sessionCreationPolicy(SessionCreationPolicy.STATELESS)
            .and()
            .authorizeHttpRequests(auth -> auth
                .requestMatchers("/api/v1/public/**").permitAll()
                .requestMatchers("/actuator/health").permitAll()
                .requestMatchers(HttpMethod.POST, "/api/v1/tasks").hasRole("OPERATOR")
                .requestMatchers(HttpMethod.GET, "/api/v1/search/**").hasRole("VIEWER")
                .requestMatchers("/api/v1/admin/**").hasRole("ADMIN")
                .anyRequest().authenticated()
            )
            .oauth2ResourceServer(oauth2 -> oauth2
                .jwt(jwt -> jwt
                    .jwtAuthenticationConverter(jwtAuthenticationConverter())
                )
            )
            .build();
    }
    
    @Bean
    public JwtAuthenticationConverter jwtAuthenticationConverter() {
        JwtGrantedAuthoritiesConverter authoritiesConverter = 
            new JwtGrantedAuthoritiesConverter();
        authoritiesConverter.setAuthorityPrefix("ROLE_");
        authoritiesConverter.setAuthoritiesClaimName("authorities");
        
        JwtAuthenticationConverter converter = new JwtAuthenticationConverter();
        converter.setJwtGrantedAuthoritiesConverter(authoritiesConverter);
        return converter;
    }
}

API Rate Limiting:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
@Component
public class RateLimitingFilter implements Filter {
    
    @Autowired
    private RedisTemplate<String, String> redisTemplate;
    
    private final Map<String, RateLimitConfig> rateLimitConfigs = Map.of(
        "/api/v1/search", new RateLimitConfig(100, Duration.ofMinutes(1)),
        "/api/v1/tasks", new RateLimitConfig(50, Duration.ofMinutes(1)),
        "/api/v1/similarity", new RateLimitConfig(20, Duration.ofMinutes(1))
    );
    
    @Override
    public void doFilter(ServletRequest request, ServletResponse response, 
                        FilterChain chain) throws IOException, ServletException {
        
        HttpServletRequest httpRequest = (HttpServletRequest) request;
        HttpServletResponse httpResponse = (HttpServletResponse) response;
        
        String clientId = extractClientId(httpRequest);
        String endpoint = httpRequest.getRequestURI();
        
        RateLimitConfig config = getRateLimitConfig(endpoint);
        if (config != null && !isRequestAllowed(clientId, endpoint, config)) {
            httpResponse.setStatus(HttpStatus.TOO_MANY_REQUESTS.value());
            httpResponse.getWriter().write("Rate limit exceeded");
            return;
        }
        
        chain.doFilter(request, response);
    }
    
    private boolean isRequestAllowed(String clientId, String endpoint, RateLimitConfig config) {
        String key = "rate_limit:" + clientId + ":" + endpoint;
        String currentCount = redisTemplate.opsForValue().get(key);
        
        if (currentCount == null) {
            redisTemplate.opsForValue().set(key, "1", config.getWindow());
            return true;
        }
        
        int count = Integer.parseInt(currentCount);
        if (count >= config.getLimit()) {
            return false;
        }
        
        redisTemplate.opsForValue().increment(key);
        return true;
    }
}

Advanced Use Cases and Examples

Real-time Traffic Monitoring:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
@Service
public class TrafficMonitoringService {
    
    @EventListener
    public void handleVehicleDetection(VehicleDetectedEvent event) {
        VehicleObject vehicle = event.getVehicle();
        
        // Check for traffic violations
        if (isSpeedViolation(vehicle)) {
            publishSpeedViolationAlert(vehicle);
        }
        
        // Update traffic flow statistics
        updateTrafficFlow(vehicle.getLocation(), vehicle.getTimestamp());
        
        // Check for congestion patterns
        if (detectTrafficCongestion(vehicle.getLocation())) {
            publishTrafficAlert(vehicle.getLocation());
        }
    }
    
    private boolean isSpeedViolation(VehicleObject vehicle) {
        SpeedLimit speedLimit = getSpeedLimit(vehicle.getLocation());
        double estimatedSpeed = calculateSpeed(vehicle);
        
        return estimatedSpeed > speedLimit.getLimit() * 1.1; // 10% tolerance
    }
    
    private void publishSpeedViolationAlert(VehicleObject vehicle) {
        SpeedViolationAlert alert = SpeedViolationAlert.builder()
            .vehicleId(vehicle.getId())
            .plateNumber(vehicle.getPlateNumber())
            .location(vehicle.getLocation())
            .timestamp(vehicle.getTimestamp())
            .estimatedSpeed(calculateSpeed(vehicle))
            .build();
            
        kafkaTemplate.send("speed-violations", alert);
    }
}

Crowd Density Analysis:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
@Component
public class CrowdAnalysisProcessor {
    
    private final int CROWD_DENSITY_THRESHOLD = 10; // persons per square meter
    
    @Scheduled(fixedRate = 30000) // Every 30 seconds
    public void analyzeCrowdDensity() {
        List<CameraLocation> locations = cameraService.getAllActiveLocations();
        
        locations.parallelStream().forEach(location -> {
            List<PersonObject> recentDetections = getRecentPersonDetections(
                location, Duration.ofMinutes(1));
            
            double density = calculateCrowdDensity(recentDetections, location.getArea());
            
            if (density > CROWD_DENSITY_THRESHOLD) {
                CrowdDensityAlert alert = CrowdDensityAlert.builder()
                    .location(location)
                    .density(density)
                    .personCount(recentDetections.size())
                    .timestamp(Instant.now())
                    .severity(determineSeverity(density))
                    .build();
                
                alertService.publishAlert(alert);
            }
            
            // Store density metrics
            metricsService.recordCrowdDensity(location, density);
        });
    }
    
    private double calculateCrowdDensity(List<PersonObject> persons, double area) {
        // Remove duplicates based on spatial proximity
        List<PersonObject> uniquePersons = removeDuplicateDetections(persons);
        return uniquePersons.size() / area;
    }
    
    private List<PersonObject> removeDuplicateDetections(List<PersonObject> persons) {
        List<PersonObject> unique = new ArrayList<>();
        
        for (PersonObject person : persons) {
            boolean isDuplicate = unique.stream()
                .anyMatch(p -> calculateDistance(p.getBbox(), person.getBbox()) < 50);
                
            if (!isDuplicate) {
                unique.add(person);
            }
        }
        
        return unique;
    }
}

Behavioral Pattern Recognition:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
@Service
public class BehaviorAnalysisService {
    
    @Autowired
    private PersonTrackingService trackingService;
    
    public void analyzePersonBehavior(List<PersonObject> personHistory) {
        PersonTrajectory trajectory = trackingService.buildTrajectory(personHistory);
        
        // Detect loitering behavior
        if (detectLoitering(trajectory)) {
            BehaviorAlert alert = BehaviorAlert.builder()
                .personId(trajectory.getPersonId())
                .behaviorType(BehaviorType.LOITERING)
                .location(trajectory.getCurrentLocation())
                .duration(trajectory.getDuration())
                .confidence(0.85)
                .build();
                
            alertService.publishBehaviorAlert(alert);
        }
        
        // Detect suspicious movement patterns
        if (detectSuspiciousMovement(trajectory)) {
            BehaviorAlert alert = BehaviorAlert.builder()
                .personId(trajectory.getPersonId())
                .behaviorType(BehaviorType.SUSPICIOUS_MOVEMENT)
                .movementPattern(trajectory.getMovementPattern())
                .confidence(calculateConfidence(trajectory))
                .build();
                
            alertService.publishBehaviorAlert(alert);
        }
    }
    
    private boolean detectLoitering(PersonTrajectory trajectory) {
        // Check if person stayed in same area for extended period
        Duration stationaryTime = trajectory.getStationaryTime();
        double movementRadius = trajectory.getMovementRadius();
        
        return stationaryTime.toMinutes() > 10 && movementRadius < 5.0;
    }
    
    private boolean detectSuspiciousMovement(PersonTrajectory trajectory) {
        // Analyze movement patterns for suspicious behavior
        MovementPattern pattern = trajectory.getMovementPattern();
        
        return pattern.hasErraticMovement() || 
               pattern.hasUnusualDirectionChanges() ||
               pattern.isCounterFlow();
    }
}

Interview Questions and Insights

Technical Architecture Questions:

Q: How do you ensure data consistency when processing high-volume video streams?

A: Implement event sourcing with Kafka as the source of truth, use idempotent message processing with unique frame IDs, implement exactly-once semantics in Kafka consumers, and use distributed locking for critical sections. Apply the saga pattern for complex workflows and maintain event ordering through partitioning strategies.

Q: How would you optimize GPU utilization across multiple analysis nodes?

A: Implement dynamic batching to maximize GPU throughput, use GPU memory pooling to reduce allocation overhead, implement model quantization for faster inference, use multiple streams per GPU for concurrent processing, and implement intelligent load balancing based on GPU memory and compute utilization.

Q: How do you handle camera failures and ensure continuous monitoring?

A: Implement health checks with circuit breakers, maintain redundant camera coverage for critical areas, use automatic failover mechanisms, implement camera status monitoring with alerting, and maintain a hot standby system for critical infrastructure.

Scalability and Performance Questions:

Q: How would you scale this system to handle 10,000 concurrent camera streams?

A: Implement horizontal scaling with container orchestration (Kubernetes), use streaming data processing frameworks (Apache Flink/Storm), implement distributed caching strategies, use database sharding and read replicas, implement edge computing for preprocessing, and use CDN for static content delivery.

Q: How do you optimize search performance for billions of detection records?

A: Implement data partitioning by time and location, use Elasticsearch with proper index management, implement caching layers with Redis, use approximate algorithms for similarity search, implement data archiving strategies, and use search result pagination with cursor-based pagination.

Data Management Questions:

Q: How do you handle privacy and data retention in video analytics?

A: Implement data anonymization techniques, use automatic data expiration policies, implement role-based access controls, use encryption for data at rest and in transit, implement audit logging for data access, and ensure compliance with privacy regulations (GDPR, CCPA).

Q: How would you implement real-time similarity search for millions of face vectors?

A: Use approximate nearest neighbor algorithms (LSH, FAISS), implement hierarchical indexing, use vector quantization techniques, implement distributed vector databases (Milvus, Pinecone), use GPU acceleration for vector operations, and implement caching for frequently accessed vectors.

External Resources and References

Technical Documentation:

AI/ML Resources:

Monitoring and Observability:

Container Orchestration:

This comprehensive platform design provides a production-ready solution for video analytics with proper scalability, performance optimization, and maintainability considerations. The architecture supports both small-scale deployments and large-scale enterprise installations through its modular design and containerized deployment strategy.