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Long-term object tracking algorithm with occlusion-awareness and re-detection
Author(s): Zhongke Li; Xiaoping Su; Changsheng Wan
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Paper Abstract

To solve the problem of target loss as occlusion for a variety of Correlation Filter based trackers, an improved tracking algorithm is proposed based on occlusion awareness and target re-detection mechanism in this paper, in which the occlusion awareness module is used to evaluate whether the tracked object is occluded or whether the tracking result is reliable. As the events as occlusion that results in tracking failure occur, the object re-detection module is triggered to redetect the original tracking target based on integral map of pixel-wise object confidence from color information. Furthermore, when the tracking quality is unreliable and no reliable object is re-detected, and the tracking model is not updated. Experiments show that the proposed algorithm can effectively avoid the problem of the Correlation Filter tracker’s variants, loss of the tracked object and model drift caused by occlusion, its tracking performance is obviously improved compared with that of several state-of-the-arts Correlation Filter tracker’s variants.

Paper Details

Date Published: 9 August 2018
PDF: 8 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060V (9 August 2018); doi: 10.1117/12.2502837
Show Author Affiliations
Zhongke Li, Nanjing Institute of Industry Technology (China)
Xiaoping Su, Nanjing Institute of Industry Technology (China)
Changsheng Wan, Southeast Univ. (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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