An Approach for Multi-Object Tracking with Two-Stage Min-Cost Flow
Huining Li Yalong Jiang Xianlin Zeng Feng Li Zhipeng Wang
The minimum network flow algorithm is widely used in multi-target tracking.
However, the majority of the present methods concentrate exclusively on
minimizing cost functions whose values may not indicate accurate solutions
under occlusions. In this paper, by exploiting the properties of tracklets
intersections and low-confidence detections, we develop a two-stage tracking
pipeline with an intersection mask that can accurately locate inaccurate
tracklets which are corrected in the second stage. Specifically, we employ the
minimum network flow algorithm with high-confidence detections as input in the
first stage to obtain the candidate tracklets that need correction. Then we
leverage the intersection mask to accurately locate the inaccurate parts of
candidate tracklets. The second stage utilizes low-confidence detections that
may be attributed to occlusions for correcting inaccurate tracklets. This
process constructs a graph of nodes in inaccurate tracklets and low-confidence
nodes and uses it for the second round of minimum network flow calculation. We
perform sufficient experiments on popular MOT benchmark datasets and achieve
78.4 MOTA on the test set of MOT16, 79.2 on MOT17, and 76.4 on MOT20, which
shows that the proposed method is effective.