Title:
Vehicle Tracking and Re-identification: A Smart Approach to Security and Civic Monitoring
Authors:
Shailendra Singh Kathait, Ashish Kumar, Samay Sawal, Arjun Dhavse, Kimaya Pundir
Summary:
This research paper presents a modular, computer vision–driven system for real-time vehicle tracking, re-identification, and overspeeding violation detection using monocular traffic surveillance cameras. The proposed framework integrates two complementary components: a deep feature–based Vehicle Re-Identification (ReID) module and a YOLOv8-powered speed estimation pipeline. The ReID module leverages a Vision Transformer backbone to extract 512-dimensional embeddings from vehicle images captured at temporal intervals, enabling robust identity association across time despite changes in viewpoint, illumination, and partial occlusions. In parallel, the speed estimation module detects vehicles at fixed sampling intervals, tracks centroid displacement within a defined region of interest, and converts pixel-level motion into real-world speed using calibrated scaling and Kalman filtering for noise reduction. Vehicles exceeding a predefined speed threshold are automatically flagged and visually annotated, while detailed identity-matching logs are generated for audit and enforcement purposes. Experimental evaluation on real-world CCTV footage demonstrates stable detection, accurate identity continuity, and reliable speed violation reporting under realistic urban traffic conditions. The system’s modular architecture supports scalability, adaptability, and future enhancements such as automated camera calibration, cross-camera tracking, and edge deployment, making it well suited for smart city traffic monitoring and civic security applications
