  Online multi-object tracking (MOT) is a longstanding task for computer vision
and intelligent vehicle platform. At present, the main paradigm is
tracking-by-detection, and the main difficulty of this paradigm is how to
associate current candidate detections with historical tracklets. However, in
the MOT scenarios, each historical tracklet is composed of an object sequence,
while each candidate detection is just a flat image, which lacks temporal
features of the object sequence. The feature difference between current
candidate detections and historical tracklets makes the object association much
harder. Therefore, we propose a Spatial-Temporal Mutual Representation Learning
(STURE) approach which learns spatial-temporal representations between current
candidate detections and historical sequences in a mutual representation space.
For historical trackelets, the detection learning network is forced to match
the representations of sequence learning network in a mutual representation
space. The proposed approach is capable of extracting more distinguishing
detection and sequence representations by using various designed losses in
object association. As a result, spatial-temporal feature is learned mutually
to reinforce the current detection features, and the feature difference can be
relieved. To prove the robustness of the STURE, it is applied to the public MOT
challenge benchmarks and performs well compared with various state-of-the-art
online MOT trackers based on identity-preserving metrics.

Computer Vision and Pattern Recognition