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

Abstract

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.