Reinforce Model Tracklet for Multi-Object Tracking

Recently, most multi-object tracking algorithms adopt the idea of tracking-by-detection. Related studies have shown that significant improvements with the development of detectors. However, missed detection and false detection are more serious in occlusion situations. Therefore, the tracker uses tracklet (short trajectories) to generate more perfect trajectories. There are many tracklet generation algorithms, but the fragmentation problem is still prevalent in crowded scenes. Fixed window tracklet generation strategies are not suitable for dynamic environments with occlusions. To solve this problem, we propose a reinforcement learning-based framework for tracklet generation, where we regard tracklet generation as a Markov decision process and then utilize reinforcement learning to dynamically predict the window size for generating tracklet. Additionally, we introduce a novel scheme that incorporates the temporal order of tracklet for association. Experiments of our method on the MOT17 dataset demonstrate its effectiveness, achieving competitive results compared to the most advanced methods.