  Most modern multi-object tracking (MOT) systems follow the
tracking-by-detection paradigm. It first localizes the objects of interest,
then extracting their individual appearance features to make data association.
The individual features, however, are susceptible to the negative effects as
occlusions, illumination variations and inaccurate detections, thus resulting
in the mismatch in the association inference. In this work, we propose to
handle this problem via making full use of the neighboring information. Our
motivations derive from the observations that people tend to move in a group.
As such, when an individual target's appearance is seriously changed, we can
still identify it with the help of its neighbors. To this end, we first utilize
the spatio-temporal relations produced by the tracking self to efficiently
select suitable neighbors for the targets. Subsequently, we construct neighbor
graph of the target and neighbors then employ the graph convolution networks
(GCN) to learn the graph features. To the best of our knowledge, it is the
first time to exploit neighbor cues via GCN in MOT. Finally, we test our
approach on the MOT benchmarks and achieve state-of-the-art performance in
online tracking.
