  Multiple Object Tracking (MOT) is an important task in computer vision. MOT
is still challenging due to the occlusion problem, especially in dense scenes.
Following the tracking-by-detection framework, we propose the Box-Plane
Matching (BPM) method to improve the MOT performacne in dense scenes. First, we
design the Layer-wise Aggregation Discriminative Model (LADM) to filter the
noisy detections. Then, to associate remaining detections correctly, we
introduce the Global Attention Feature Model (GAFM) to extract appearance
feature and use it to calculate the appearance similarity between history
tracklets and current detections. Finally, we propose the Box-Plane Matching
strategy to achieve data association according to the motion similarity and
appearance similarity between tracklets and detections. With the effectiveness
of the three modules, our team achieves the 1st place on the Track-1
leaderboard in the ACM MM Grand Challenge HiEve 2020.
