Online multiple object tracking based on fusing global and partial features

Abstract

Multiple object tracking (MOT) has gained increasing attention due to its academic and commercial interests in computer vision tasks. Most of the existing state-of-the-art MOT methods consider the tracking-by-detection (TBD) framework, which localizes the pedestrian in each frame and connects these object hypotheses into the trajectories without any initial labeling. These methods heavily depend on detection accuracy and data association. However, occlusion often occurs in real surveillance scenes. The frequent occlusion leads to many false detections and inaccurate appearance, decreasing the tracking performance. In this paper, we aim to propose a novel feature matching method that combines the global and partial feature matching model between two bounding boxes to improve the similarity measurement between them. Moreover, the new feature matching method leverages the advantage that global features can illustrate the whole image, and partial features can effectively handle occlusion and noise. In addition, we propose a detection modifier method based on human pose information. This detection method can be used to filter out false pedestrian detections. Finally, the experimental results demonstrate the effectiveness of our proposed method and achieve comparable performance with the state-of-the-art MOT trackers.