VisionEdge Real-time Inference
Quantized computer vision pipeline for real-time edge object detection and tracking.
System Overview & Motivation
VisionEdge is an ultra-fast computer vision inference pipeline optimized for edge devices and resource-constrained environments. Utilizing TensorRT acceleration and INT8 model quantization, it performs real-time multi-class object detection and tracking on high-definition video streams with minimal hardware requirements.
Key Architectural Highlights
- YOLOv8 pipeline compiled with NVIDIA TensorRT achieving 60+ FPS on consumer GPUs
- INT8 post-training quantization reducing model memory footprint by 4x without accuracy degradation
- Low-latency WebRTC and WebSocket streaming delivering real-time bounding boxes to browser dashboards
- Containerized microservice packaging with minimal Docker alpine runtime
- Configurable region-of-interest (ROI) virtual tripwires and anomaly detection alerts
Technical Architecture & Data Pipeline
Video feeds are ingested via OpenCV or RTSP streams into a C++/Python processing daemon. Frames are passed into a TensorRT execution engine running asynchronous batch inference. Detection coordinates and confidence scores are packaged into binary WebSocket packets broadcast to connected web clients.
Production Challenges & Edge Cases Solved
Overcoming thermal throttling and memory bandwidth saturation on small edge hardware. Solved by implementing dynamic frame dropping, circular buffer memory pools, and optimized TensorRT memory planning.