Robust Multivehicle Tracking with Wasserstein Association Metric in Surveillance Videos

Vehicle tracking based on surveillance videos is of great significance in the highway traffic monitoring field. In real-world vehicle-tracking applications, partial occlusion and objects with similarly appearing distractors pose significant challenges. For addressing the above issues, we propose a robust multivehicle tracking with Wasserstein association metric (MTWAM) method. In MTWAM, we analyze the advantage of the 1-Wasserstein distance (WD-1) on partial occlusion and employ the WD-1 as the similarity criterion to measure the similarity between tracklets and detections. Moreover, for distinguishing different objects with a similar appearance, we improve the feature presentation of vehicles by developing target-specific feature sparse coding (TSSC). To demonstrate the validity of this method, we present a quantitative evaluation of both the UA-DETRAC dataset and our vehicle highway surveillance videos dataset (VecHSV). In both cases, our method achieves state-of-the-art performances.