Improving model drift for robust object tracking

• Published in: Multimedia tools and applications Vol. 79; no. 35-36; pp. 25801 - 25815
• Main Authors: Dong, Qiujie, He, Xuedong, Ge, Haiyan, Liu, Qin, Han, Aifu, Zhou, Shengzong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2020; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Confidence; Data Structures and Information Theory; Datasets; Drift; Multimedia; Multimedia Information Systems; Robustness; Special Purpose and Application-Based Systems; Tracking; Primary and secondary peaks detection; Confidence function; Object tracking; Adaptive discriminant; Correlation filters; Adaptive merge
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09032-z

Discriminative correlation filters show excellent performance in object tracking. However, in complex scenes, the apparent characteristics of the tracked target are variable, which makes it easy to pollute the model and cause the model drift. In this paper, considering that the secondary peak has a greater impact on the model update, we propose a method for detecting the primary and secondary peaks of the response map. Secondly, a novel confidence function which uses the adaptive update discriminant mechanism is proposed, which yield good robustness. Thirdly, we propose a robust tracker with correlation filters, which uses hand-crafted features and can improve model drift in complex scenes. Finally, in order to cope with the current trackers’ multi-feature response merge, we propose a simple exponential adaptive merge approach. Extensive experiments are performed on OTB2013, OTB100 and TC128 datasets. Our approach performs superiorly against several state-of-the-art trackers while runs in real-time.