Center-point-pair detection and context-aware re-identification for end-to-end multi-object tracking

Abstract

Online multi-object tracking aims at generating the trajectories for multiple objects in the surveillance scene. It remains a challenging problem in crowded scenes because objects often gather together and occlude in tracking frames. The main impact of crowd occlusions is that it severely harms the performance of the detector and significantly increases the difficulty in extracting object features. In this paper, we propose an end-to-end tracking framework that alleviates such issues and estimates more accurate trajectories. Firstly, We design a Center-Point-Pair detection branch for object detection, which learns the correlations between the object head and the body to simultaneously predict the head and body regions to alleviate unreliable detection in tracking scenes. Secondly, we introduce the context information around the object to the tracker, inspired by the human search pattern. We propose a Context-Aware Re-Identification branch that includes the Previous-Frame Guided Spatial-Attention Model and the Previous-Frame Guided Channel-Attention Model to extract more discriminative object features. Thirdly, to harness the power of deep features for data association in generating reliable trajectories, we propose the Similarity Cluster Trajectory Management method that expands affinity descriptor and adopts the minority obeying the majority principle to association trajectories and detections. The experiments on diverse and challenging MOT datasets show that our tracking framework achieves superior results compared to other state-of-the-arts offline and online multi-object tracking methods.