Opportunities and Challenges in Vehicle Tracking: A Computer Vision-Based Vehicle Tracking System

Abstract

This study proposes a tracking system for the transportation field utilizing state-of-the-art detection heads and tracking algorithm. We utilize detection heads (YOLO5, FRCNN, DETR) with tracking algorithms (ByteTrack) under different camera settings and three weather scenarios. The tracking results are summarized regarding Recall and precision, IDF1, IDP, IDR, MOTA, MOTP, tracking consistency, and processing time. In this study, we utilize novel Transformer architecture, a deep learning network based on the attention mechanism. Due to its strong capabilities, attention architecture has recently attracted a huge amount of attention in computer vision. This analysis showed that DETR was outperforming other algorithms in terms of tracking consistency, and it was performing similarly to FRCNN in Recall with better performance at the camera installed on intersections. Both FRCNN and DETR were outperforming YOLO5 in terms of Recall. YOLO5 is almost outperforming other algorithms in terms of Precision, MOTA, IDF1, and inference time. Considering intersections and highways, this paper has practical significance in intelligent traffic management and control.