Detector-tracker integration framework and attention mechanism for multi-object tracking

Chunjiang Li, Guangzhu Chen, Rongsong Gou, Zaizuo Tang

Abstract: Online multi-object tracking is a process of extending multi-object trajectories with only past information. In this process, tracking drift, missing detection, and occlusion among objects are common problems. To address the problem of tracking drift, in this paper, a detector–tracker integration framework is proposed and the framework includes the linear regression model (LRM) that can detect and track objects simultaneously. The two kinds of results are combined using LRM to suppress tracking drift. Moreover, to overcoming missing detection and the occlusion, a structural similarity calculation based on the attention mechanism is proposed. The attention mechanism is utilized to learn the discriminative features of each object, and a structural similarity calculation are used to improve the ability to extend the objects’ trajectories. Based on these, we design a multi-object tracking strategy, enabling the trajectory’s initialization, extension, and termination. Finally, experiments and analysis are executed on MOT16 and MOT17 benchmarks datasets, and the proposed method obtains multi-object tracking accuracy of 60%.