Dr. Yanxia Wei Profile

Dr. Yanxia Wei

at Shanghai Univ

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Publications (1)

Proceedings Article | 16 October 2019 Paper

A robust visual tracking via nonlocal correlation filters

Yanxia Wei, Zhen Jiang, Dongxun Chen

Proceedings Volume 11205, 112050R (2019) https://doi.org/10.1117/12.2542125

KEYWORDS: Image filtering, Electronic filtering, Optical tracking, Visualization, Video, Detection and tracking algorithms, Mechanical engineering, Reliability, Statistical analysis, Machine vision

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Visual object tracking has become increasingly popular in the community due to its application and research significance. However, occlusion is one of the major factors that seriously impact the tracking performance in visual tracking. To address this issue, in this paper, we propose a novel nonlocal correlation filter based tracking method. Our proposed tracker effectively exploits the explicit coupled mechanism which depends on the global filter and several local part filters, and efficiently employs the spatial geometric constraints among the global object and local patches of object for preserving the structure of object. Compared with other existing correlation filter based trackers, our proposed tracking method has three advantages: (1) To ensure complete representation to the target candidate, we learn the correlation filers from not only the global sample but also local sample parts. The global based filter guarantees the overall accuracy of the tracked object, while the local based filters reserve the details of tracking object to cope with the challenging cases like occlusion or deformation. In addition, an effective and adaptive selection mechanism is proposed to select the most distinctive and discriminative parts for tracking, which avoids unnecessary computing burden caused by tracking all parts and simultaneously improves the robustness of the tracker. (2) Through adaptively weighting the global sample and each local part of samples, the integration mechanism puts more emphasis on visible parts and eliminates the impacts by occluded parts for further improving the tracking robustness. (3) Different from other trackers by searching for the predefined scale pyramid, we propose a simple yet effective scale estimation strategy which can accurately calculate the current scale of the tracking target. For verifying our method, we conduct extensive qualitative and quantitative experiments on challenging benchmark image sequences. Experiment results demonstrate that our proposed method performs favorably against several state-of-the-art trackers.

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