A generic MOT boosting framework by combining cues from SOT, tracklet and re-identification

In this paper, we propose a generic boosting framework for multiple object tracking (MOT). Unlike other works tracking objects from zero, our framework uses their results (tracklets) and makes further optimizations. The motivation of us derives from the observation that most modern MOT trackers have been acceptable performance and can yield relatively reliable tracklets; accordingly, we straight focus on the tracklet-level re-identification, which is the most challenging issue in this case. To achieve that goal, we simultaneously utilize the techniques of single object tracking, tracking fragment (tracklets) and re-identification mechanism through casting them into a multi-label energy optimization and then innovatively solving it using the $$\alpha -$$ expansion with label costs algorithm. All these techniques inspire recent MOT a lot to mitigate the occlusion problem, but to our knowledge, by far few works explore to reasonably combine them all like us. Furthermore, we introduce a spatial attention to improve the appearance model and a hierarchical clustering as post-process to progressively improve the tracking consistency. Finally, testing results on the most used benchmarks demonstrate the significant effectiveness and generality of our framework, and the importance of each contribution is also verified through ablative studies.