Approximating Relu Networks By Single-Spike Computation

in multi-object tracking (MOT), many tracking-by-detection methods have been proposed, and intersection over union (IoU) is the most common re-identification (re-ID) method. However, IoU does not care about target motion and so that weak for large change of position, size, or disturbances that often occur in MOT scenes. in addition, only detections with a certain level of confidence are used for re-ID to avoid noise, which also exclude some positive detections and lead to fragmentation of a tracklet. in this paper, we propose a robust MOT method that represents the target motion by a combining multiple indices and performing re-ID by allowing uncertainty in each index which means one of them does not have to be satisfied for re-ID to deal with such changes and disturbances. Our re-ID also makes it possible to effectively utilize positive detections that were previously excluded, achieving top performance in comprehensive metrics MOTA, IDF1, and HOTA, and in robustness metrics such as AssA, Frag, and MT comparing to the state-of-the-art methods in MOT17 benchmark.