  Recent progresses in model-free single object tracking (SOT) algorithms have
largely inspired applying SOT to \emph{multi-object tracking} (MOT) to improve
the robustness as well as relieving dependency on external detector. However,
SOT algorithms are generally designed for distinguishing a target from its
environment, and hence meet problems when a target is spatially mixed with
similar objects as observed frequently in MOT. To address this issue, in this
paper we propose an instance-aware tracker to integrate SOT techniques for MOT
by encoding awareness both within and between target models. In particular, we
construct each target model by fusing information for distinguishing target
both from background and other instances (tracking targets). To conserve
uniqueness of all target models, our instance-aware tracker considers response
maps from all target models and assigns spatial locations exclusively to
optimize the overall accuracy. Another contribution we make is a dynamic model
refreshing strategy learned by a convolutional neural network. This strategy
helps to eliminate initialization noise as well as to adapt to the variation of
target size and appearance. To show the effectiveness of the proposed approach,
it is evaluated on the popular MOT15 and MOT16 challenge benchmarks. On both
benchmarks, our approach achieves the best overall performances in comparison
with published results.
