Vision AI
Agents
Multi-stage computer vision pipeline that processes live video feeds in real-time. From raw RTSP streams to actionable detections — person tracking, object classification, skeleton extraction, and optical flow analysis.
What It Does
End-to-end video understanding from raw camera feeds to structured detections.
RTSP Stream Acquisition
Connects to live RTSP camera feeds with H.264/H.265 decoding. Supports multi-camera setups with frame normalization, resolution standardization, and color space conversion for consistent downstream processing.
Person & Object Detection
YOLOv8/v10-based detection engine identifies people, objects, and regions of interest in real-time. Extracts bounding boxes, confidence scores, and classification labels at inference speeds under 50ms.
Multi-Object Tracking
Persistent identity tracking across frames using deep association metrics. Handles occlusion, re-identification, and trajectory prediction for reliable tracking across complex scenes.
Optical Flow Analysis
Dense and sparse optical flow computation for motion estimation. Detects movement patterns, velocity vectors, and directional flow across the scene for behavior and anomaly detection.
Skeleton Extraction
Real-time keypoint extraction maps the human body into a 17-point skeleton. Enables pose estimation, gesture recognition, and body language analysis without facial identification.
Real-Time Inference Engine
GPU-accelerated model serving with batched inference, dynamic load balancing, and model versioning. Processes multiple camera streams concurrently with consistent sub-50ms latency.
Pipeline Architecture
The multi-stage processing pipeline from input to output.
| Stage | Technology | Details |
|---|---|---|
| Input Layer | RTSP / FFmpeg | Live video stream acquisition, H.264/H.265 decoding, multi-camera multiplexing |
| Frame Normalization | OpenCV / NumPy | Resolution normalization, color space conversion (BGR→RGB), frame rate standardization |
| Person Detection | YOLOv8 / YOLOv10 | Real-time body detection with bounding boxes, confidence scoring, keypoint extraction |
| Object Detection | YOLOv8 / YOLOv10 | Multi-class object classification, region-of-interest identification, spatial mapping |
| Object Tracker | DeepSORT / ByteTrack | Persistent ID assignment, re-identification across frames, trajectory prediction |
| Optical Flow | RAFT / Farneback | Dense motion vectors, movement velocity estimation, directional flow analysis |
| Skeleton Tracking | YOLOv8-Pose | 17-point keypoint extraction, pose estimation, body orientation detection |
| Inference Engine | TensorRT / ONNX | GPU-accelerated serving, batched inference, dynamic model loading, <50ms latency |
| Data Layer | PostgreSQL + Redis | Detection logs, tracking history, analytics aggregation, real-time event cache |
| Production Pipeline | Kafka + Docker | Stream processing, horizontal scaling, real-time alerts, monitoring dashboards |
How a Frame Is Processed
Stream Captured
RTSP feed decoded into raw frames at native resolution
Normalized
Resolution, color space, and frame rate standardized
Objects Detected
YOLOv8/v10 identifies people, objects, and regions
Tracked & Mapped
Persistent IDs, trajectories, and skeleton extraction
Data Stored
Detections logged, analytics computed, alerts triggered
Tech Stack
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