  Most existing Multi-Object Tracking (MOT) approaches follow the
Tracking-by-Detection paradigm and the data association framework where objects
are firstly detected and then associated. Although deep-learning based method
can noticeably improve the object detection performance and also provide good
appearance features for cross-frame association, the framework is not
completely end-to-end, and therefore the computation is huge while the
performance is limited. To address the problem, we present a completely
end-to-end approach that takes image-sequence/video as input and outputs
directly the located and tracked objects of learned types. Specifically, with
our introduced multi-object representation strategy, a global response map can
be accurately generated over frames, from which the trajectory of each tracked
object can be easily picked up, just like how a detector inputs an image and
outputs the bounding boxes of each detected object. The proposed model is fast
and accurate. Experimental results based on the MOT16 and MOT17 benchmarks show
that our proposed on-line tracker achieved state-of-the-art performance on
several tracking metrics.
