  Recent works have shown that convolutional networks have substantially
improved the performance of multiple object tracking by simultaneously learning
detection and appearance features. However, due to the local perception of the
convolutional network structure itself, the long-range dependencies in both the
spatial and temporal cannot be obtained efficiently. To incorporate the spatial
layout, we propose to exploit the local correlation module to model the
topological relationship between targets and their surrounding environment,
which can enhance the discriminative power of our model in crowded scenes.
Specifically, we establish dense correspondences of each spatial location and
its context, and explicitly constrain the correlation volumes through
self-supervised learning. To exploit the temporal context, existing approaches
generally utilize two or more adjacent frames to construct an enhanced feature
representation, but the dynamic motion scene is inherently difficult to depict
via CNNs. Instead, our paper proposes a learnable correlation operator to
establish frame-to-frame matches over convolutional feature maps in the
different layers to align and propagate temporal context. With extensive
experimental results on the MOT datasets, our approach demonstrates the
effectiveness of correlation learning with the superior performance and obtains
state-of-the-art MOTA of 76.5% and IDF1 of 73.6% on MOT17.
