  Multiple Object Tracking (MOT) plays an important role in solving many
fundamental problems in video analysis in computer vision. Most MOT methods
employ two steps: Object Detection and Data Association. The first step detects
objects of interest in every frame of a video, and the second establishes
correspondence between the detected objects in different frames to obtain their
tracks. Object detection has made tremendous progress in the last few years due
to deep learning. However, data association for tracking still relies on hand
crafted constraints such as appearance, motion, spatial proximity, grouping
etc. to compute affinities between the objects in different frames. In this
paper, we harness the power of deep learning for data association in tracking
by jointly modelling object appearances and their affinities between different
frames in an end-to-end fashion. The proposed Deep Affinity Network (DAN)
learns compact; yet comprehensive features of pre-detected objects at several
levels of abstraction, and performs exhaustive pairing permutations of those
features in any two frames to infer object affinities. DAN also accounts for
multiple objects appearing and disappearing between video frames. We exploit
the resulting efficient affinity computations to associate objects in the
current frame deep into the previous frames for reliable on-line tracking. Our
technique is evaluated on popular multiple object tracking challenges MOT15,
MOT17 and UA-DETRAC. Comprehensive benchmarking under twelve evaluation metrics
demonstrates that our approach is among the best performing techniques on the
leader board for these challenges. The open source implementation of our work
is available at https://github.com/shijieS/SST.git.
