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8 March 2018 Discriminative correlation filter tracking with occlusion detection
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106090X (2018)
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Aiming at the problem that the correlation filter-based tracking algorithm can not track the target of severe occlusion, a target re-detection mechanism is proposed. First of all, based on the ECO, we propose the multi-peak detection model and the response value to distinguish the occlusion and deformation in the target tracking, which improve the success rate of tracking. And then we add the confidence model to update the mechanism to effectively prevent the model offset problem which due to similar targets or background during the tracking process. Finally, the redetection mechanism of the target is added, and the relocation is performed after the target is lost, which increases the accuracy of the target positioning. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art methods in terms of robustness and accuracy.
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Shuo Zhang, Zhong Chen, XiPeng Yu, Ting Zhang, and Jing He "Discriminative correlation filter tracking with occlusion detection", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106090X (8 March 2018);


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