An end-to-end identity association network based on geometry refinement for multi-object tracking. (September 2022)

• Record Type: Journal Article
• Authors: Li, Rui; Zhang, Baopeng; Teng, Zhu; Fan, Jianping
• Is Part Of: Pattern recognition. Volume 129(2022)
• Publication Date: 2022-09
• Subjects: Multi-object tracking -- Interactions -- Occlusions -- Data association -- Identity verification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108738
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: In multi-target tracking, object interactions and occlusions are two significant factors that affect tracking performance. To settle this, we propose an identity association network (IANet) that integrates the geometry refinement network (GRNet) and the identity verification (IV) module to perform data association and reason the mapping between the detections and tracklets. In our data association process, the object drifts caused by object interactions are suppressed effectively by encoding the direction and velocity of objects to refine the geometric position of tracklets. The tracklets with refined geometric information are further utilized in the IV module to achieve a sufficient encoding of multivariate spatial cues including both appearance and geometry information, which defeats the misleading impacts of interactions and occlusions dramatically in multi-object tracking. The extensive experiments and comparative evaluations have demonstrated that our proposed method can significantly outperform many state-of-the-art methods on benchmarks of 2D MOT2015, MOT16, MOT17, MOT20, and KITTI by using public detection and online settings.