Smart Traffic Inference System
Built an end-to-end edge AI system that analyzes live CCTV feeds to monitor traffic density and vehicle types. Utilized YOLOv8 for object detection and DeepSORT for multi-object tracking. The inference ran on NVIDIA Jetson devices at the edge, sending aggregated telemetry back to a centralized cloud dashboard built with React and Node.js for city planners.
01 / The Challenge
City planners lacked real-time, granular data on traffic congestion and vehicle class distribution at key intersections.
- 01Optimizing the heavy YOLOv8 model to run in real-time on resource-constrained edge hardware.
- 02Handling varying lighting conditions, weather (rain, fog), and occlusions in CCTV feeds.
- 03Ensuring robust offline-first capabilities if the edge device lost cloud connectivity.
02 / System Architecture
Deployed lightweight, optimized edge AI models to process video feeds locally, saving immense bandwidth costs.
Python, YOLOv8, TensorRT, NVIDIA Jetson, Node.js, React, WebSocket.
TECHNOLOGY STACK
03 / Measurable Outcomes
Reduced cloud bandwidth costs by 95% (only sending metadata) and maintained 30 FPS inference at the edge.