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Enhanced online convolutional neural networks for object tracking
Author(s): Dengzhuo Zhang; Yun Gao; Hao Zhou; Tianwen Li
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Paper Abstract

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

Paper Details

Date Published: 13 April 2018
PDF: 7 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960A (13 April 2018); doi: 10.1117/12.2310122
Show Author Affiliations
Dengzhuo Zhang, Yunnan Univ. (China)
Yun Gao, Yunnan Univ. (China)
Kunming Institute of Physics (China)
Hao Zhou, Yunnan Univ. (China)
Tianwen Li, Kunming Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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