Intelligent Multi-Source Video Analytics & Streaming Platform

Design and implement a multi-source, real-time video analytics and
streaming platform that:
- Aggregates feeds from multiple webcams or IP cameras
- Applies AI-powered analytics (e.g., person detection, motion
heatmaps) on a server
- Streams both raw and annotated video to clients using GStreamer
- Provides a REST API for dynamic control and monitoring
- Includes MQTT as an optional messaging layer for advanced
synchronization
- Offers a simple OpenCV-based client (or web page) to view streams
System Architecture
1. Video Ingestion Layer
- GStreamer pipelines capture N sources (webcam streams) in
parallel.
2. Server-Side Analytics Engine
- In Python (OpenCV + TensorFlow/PyTorch), processes each
frame for:
- Person/object detection (e.g., YOLOv5 Tiny, SSD, ...)
- Motion heatmaps or object tracking stats (optional)
- Outputs annotated frames (bounding boxes/overlays) and
metadata.
3. Streaming Server
- Uses GStreamer to publish:
- Raw video feeds
- Annotated video feeds
4. REST API Server
- Built with Flask or FastAPI
- Endpoints to:
- Start/stop individual streams
- Adjust encoding params (resolution, framerate, bitrate)
- Query system status and analytics metrics
5. Optional: MQTT Integration
- Mosquitto broker + Paho clients
- Topics for control commands and event publishes
6. Client Application
- Simple Python/OpenCV script (or HTML5 video player)
- Connects to endpoints and displays video
- (Bonus) Subscribes to MQTT topics for real-time alerts