  Most of Multiple Object Tracking (MOT) approaches compute individual target
features for two subtasks: estimating target-wise motions and conducting
pair-wise Re-Identification (Re-ID). Because of the indefinite number of
targets among video frames, both subtasks are very difficult to scale up
efficiently in end-to-end Deep Neural Networks (DNNs). In this paper, we design
an end-to-end DNN tracking approach, Flow-Fuse-Tracker (FFT), that addresses
the above issues with two efficient techniques: target flowing and target
fusing. Specifically, in target flowing, a FlowTracker DNN module learns the
indefinite number of target-wise motions jointly from pixel-level optical
flows. In target fusing, a FuseTracker DNN module refines and fuses targets
proposed by FlowTracker and frame-wise object detection, instead of trusting
either of the two inaccurate sources of target proposal. Because FlowTracker
can explore complex target-wise motion patterns and FuseTracker can refine and
fuse targets from FlowTracker and detectors, our approach can achieve the
state-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 56.5
on the MOT17 tracking benchmarks, surpassing all the online and offline methods
in existing publications.
