Dynamic update template for visual object tracking

Yuqi Lai; Manman He; Chen Yao; Zhijian Yin; Hongliang Zou; Zhen Yang

doi:10.1117/12.3048089

3 October 2024 Dynamic update template for visual object tracking

Yuqi Lai, Manman He, Chen Yao, Zhijian Yin, Hongliang Zou, Zhen Yang

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720Y (2024) https://doi.org/10.1117/12.3048089
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Continuous acquisition of the latest information about the shape of the object allows for more efficient and robust classification, as well as accurate estimation of the target state. However, previous methods have often overlooked this problem and used only the target information from the first frame in tracking. In this paper, we propose three specific and practical guidelines aimed at updating the target state, enabling the development of an anchor-free generic object tracker without requiring any prior knowledge. These guidelines offer a clear path and direction for the development process. Using these guidelines, we develop our Dynamic Update Template (DUT) tracker that includes a template, a dynamic template, and a search branch, ensures unambiguous classification scores, provides estimation quality scores, and multiplies them to obtain the pcore, which serves as the basis for updating the dynamic template. By conducting thorough analyses and ablation studies, we validate the efficacy of our proposed guidelines. Our DUT tracker achieves better performance on challenging benchmarks (LaSOT) without excessive modifications. On the extensive TrackingNet dataset, DUT attains an impressive AUC score of 82.1 while maintaining a swift frame rate exceeding 90 FPS, surpassing the threshold for real-time performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuqi Lai, Manman He, Chen Yao, Zhijian Yin, Hongliang Zou, and Zhen Yang "Dynamic update template for visual object tracking", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720Y (3 October 2024); https://doi.org/10.1117/12.3048089

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KEYWORDS

Image processing

Video

Education and training

Feature extraction

Feature fusion

Detection and tracking algorithms

Optical tracking

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