TrafficTrack: rethinking the motion and appearance cue for multi-vehicle tracking in traffic monitoring

• Published in: Multimedia systems Vol. 30; no. 4; p. 216
• Main Authors: Cai, Hui, Lin, Haifeng, Liu, Dapeng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024; Springer Nature B.V
• Subjects: Cameras; Computer Communication Networks; Computer Graphics; Computer Science; Cryptology; Data Storage Representation; Intelligent transportation systems; Kalman filters; Monitoring; Multimedia Information Systems; Multiple target tracking; Obstructions; Operating Systems; Paradigms; Regular Paper; Road conditions; Sensors; Task complexity; Tracking; Traffic flow; Trajectories; Vehicles; Tracking-by-detection; Vehicles in traffic monitoring; Motion and appearance; Multiple object tracking
• ISSN: 0942-4962; 1432-1882
• DOI: 10.1007/s00530-024-01407-8

Analyzing traffic flow based on data from traffic monitoring is an essential component of intelligent transportation systems. In most traffic scenarios, vehicles are the primary targets, so multi-object tracking of vehicles in traffic monitoring is a critical subject. In view of the current difficulties, such as complex road conditions, numerous obstructions, and similar vehicle appearances, we propose a detection-based multi-object vehicle tracking algorithm that combines motion and appearance cues. Firstly, to improve the motion prediction accuracy, we propose a Kalman filter that adaptively updates the noise according to the motion matching cost and detection confidence score, combined with exponential transformation and residuals. Then, we propose a combined distance to utilize motion and appearance cues. Finally, we present a trajectory recovery strategy to handle unmatched trajectories and detections. Experimental results on the UA-DETRAC dataset demonstrate that this method achieves excellent tracking performance for vehicle tracking tasks in traffic monitoring perspectives, meeting the practical application demands of complex traffic scenarios.