Robust Multivehicle Tracking With Wasserstein Association Metric in Surveillance Videos
Yanjie Zeng, Xinsha Fu, Lei Gao, Jiawei Zhu, Haifeng Li, Yuheng Li
Abstract
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.