Tracking as A Whole: Multi-Target Tracking by Modeling Group Behavior with Sequential Detection
This work addresses the problem of precise vehicle tracking in Intelligent Transportation Systems for traffic monitoring, but it appears incremental as it builds on existing tracking-by-detection frameworks.
The paper tackles vehicle detection and tracking at road junctions by proposing a sequential detection model to handle occlusions and modeling group behavior to manage complex interactions, with evaluation on real surveillance videos showing effective performance.
Video-based vehicle detection and tracking is one of the most important components for Intelligent Transportation Systems (ITS). When it comes to road junctions, the problem becomes even more difficult due to the occlusions and complex interactions among vehicles. In order to get a precise detection and tracking result, in this work we propose a novel tracking-by-detection framework. In the detection stage, we present a sequential detection model to deal with serious occlusions. In the tracking stage, we model group behavior to treat complex interactions with overlaps and ambiguities. The main contributions of this paper are twofold: 1) Shape prior is exploited in the sequential detection model to tackle occlusions in crowded scene. 2) Traffic force is defined in the traffic scene to model group behavior, and it can assist to handle complex interactions among vehicles. We evaluate the proposed approach on real surveillance videos at road junctions and the performance has demonstrated the effectiveness of our method.