Robust Visual Tracking via Perceptive Deep Neural Network

HOU Zhiqiang; DAI Bo; HU Dan; YU Wangsheng; CHEN Chen; FAN Shunyi

doi:10.11999/JEIT151449

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1616-1623

HOU Zhiqiang, DAI Bo, HU Dan, YU Wangsheng, CHEN Chen, FAN Shunyi. Robust Visual Tracking via Perceptive Deep Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1616-1623. doi: 10.11999/JEIT151449

Citation:

HOU Zhiqiang, DAI Bo, HU Dan, YU Wangsheng, CHEN Chen, FAN Shunyi. Robust Visual Tracking via Perceptive Deep Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1616-1623. doi: 10.11999/JEIT151449

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Robust Visual Tracking via Perceptive Deep Neural Network

doi: 10.11999/JEIT151449

Funds:

The National Natural Science Foundation of China (61175029, 61473309), The Natural Science Foundation of Shaanxi Province (2015JM6269, 2015JM6269, 2016JM6050)

Received Date: 2015-12-22
Rev Recd Date: 2016-05-04
Publish Date: 2016-07-19

Abstract

Abstract

In a visual tracking system, the feature description plays the most important role. Multi-cue fusion is an effective way to solve the tracking problem under many complex conditions. Therefore, a perceptive deep neural network based on multi parallel networks which can be triggered adaptively is proposed. Then, using the multi-cue fusion, a new tracking method based on deep learning is established, in which the target can be adaptively fragmented. The fragment decreases the input dimension, thus reducing the computation complexity. During the tracking process, the model can dynamically adjust the weights of fragments according to the reliability of them, which is able to improve the flexibility of the tracker to deal with some complex circumstances, such as target posture change, light change and occluded by other objects. Qualitative and quantitative analysis on challenging benchmark video sequences show that the proposed tracking method is robust and can track the moving target robustly.
- Visual tracking,
- Feature description,
- Deep learning,
- Perceptive deep neural network

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References(24)

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