Multi-Object Tracking With Spatial-Temporal Topology-Based Detector

Multi-object tracking is a challenging task due to the occlusion of different targets. Existing methods focus on inferring a robust and discriminative feature for data association based on the targets generated by the existing detector. Unlike existing methods that consider each target independently during generating the trajectories, we propose a novel Spatial-Temporal Topology-based Detector (STTD) algorithm that treats the target and its nearest neighbors as a cluster and introduces a topology structure to describe the dynamics of moving targets belonging to the same cluster. With the public detections and the tracked objects in the previous frame, STTD firstly refines them by regression of detector to obtain the candidate proposals in the current frame. After that, the temporal topology constraint is proposed to recover the missed objects by considering the continuity and consistency of the topological structure. Based on the assumption that the targets belonging to the same topology should have a consistent characteristic, the spatial topology constraint is proposed to remove the inaccurate targets. Then we can obtain new candidate objects and construct the cost matrix used for data association. The evaluations on three MOTChallenge benchmarks verify the effectiveness of the proposed method.