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Most of Multiple Object Tracking (MOT) approaches compute individual targetfeatures for two subtasks: estimating target-wise motions and conductingpair-wise Re-Identification (Re-ID). Because of the indefinite number oftargets among video frames, both subtasks are very difficult to scale upefficiently in end-to-end Deep Neural Networks (DNNs). In this paper, we designan end-to-end DNN tracking approach, Flow-Fuse-Tracker (FFT), that addressesthe above issues with two efficient techniques: target flowing and targetfusing. Specifically, in target flowing, a FlowTracker DNN module learns theindefinite number of target-wise motions jointly from pixel-level opticalflows. In target fusing, a FuseTracker DNN module refines and fuses targetsproposed by FlowTracker and frame-wise object detection, instead of trustingeither of the two inaccurate sources of target proposal. Because FlowTrackercan explore complex target-wise motion patterns and FuseTracker can refine andfuse targets from FlowTracker and detectors, our approach can achieve thestate-of-the-art results on several MOT benchmarks. As an online MOT approach,FFT produced the top MOTA of 46.3 on the 2DMOT15, 56.5 on the MOT16, and 56.5on the MOT17 tracking benchmarks, surpassing all the online and offline methodsin existing publications.

Multiple Object Tracking by Flowing and Fusing