Real-Time Tracking of Vehicles with Siamese Network and Backward Prediction

Tracking of vehicles is a key technique for Intelligent transportation system, which commonly follows tracking-by-detection strategy. Due to high appearance similarity among vehicles and heavy occlusion caused by busy traffic flow, a major challenge in such a tracking system is the limited performance of the underlying detector which may produce noisy detections. Consequently, Siamese network and backward prediction-based vehicle tracking approach is proposed. Siamese network based forward position prediction is designed to alleviate the interference of noisy detections, while backward prediction verification is performed to reduce the false positives arising with forward prediction. The final tracklets are obtained through weighted merging based on the detection confidence and forward prediction confidence. The experiment results demonstrate that the proposed method outperforms the state-of-the-art on the UA-DETRAC vehicle tracking dataset, as well as maintains real-time processing at an average tracking speed of 20.1fps, which can be used for real-time applications.