Online multiple object tracking using confidence score‐based appearance model learning and hierarchical data association

Abstract

The goal of multiple object tracking (MOT) is to estimate the locations of objects and maintain their identities consistently to yield their individual trajectories. MOT has been developed enormously, but it is still a challenging work due to similar appearances of different objects and occlusion by other objects or background in a complex scene. In this study, the authors propose confidence score‐based appearance model learning and hierarchical data association for MOT. First, the confidence score is used to divide associated tracklet‐detection in the first stage data association into confident and unconfident results, and in the second stage, data association is applied to unconfident tracklet‐detection to improve the performance. Furthermore, it can be employed to enhance the robustness of the appearance model and due to the fast confidence score calculation, it can balance the accuracy and processing time. The experimental results with challenging public datasets show distinct performance improvement over other state‐of‐the‐art methods and demonstrate the effect of the authors’ method for online MOT.