Robust Visual Tracking Based on Spatial Reliability Constraint

Lei PU; Xinxi FENG; Zhiqiang HOU; Wangsheng YU

doi:10.11999/JEIT180780

Volume 41 Issue 7

Jul. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(7): 1650-1657

Lei PU, Xinxi FENG, Zhiqiang HOU, Wangsheng YU. Robust Visual Tracking Based on Spatial Reliability Constraint[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1650-1657. doi: 10.11999/JEIT180780

Citation:

Lei PU, Xinxi FENG, Zhiqiang HOU, Wangsheng YU. Robust Visual Tracking Based on Spatial Reliability Constraint[J]. Journal of Electronics & Information Technology, 2019, 41(7): 1650-1657. doi: 10.11999/JEIT180780

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Robust Visual Tracking Based on Spatial Reliability Constraint

doi: 10.11999/JEIT180780

1.
Graduate College, Air Force Engineering University, Xi’an 710077, China
2.
Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
3.
School of Computer Science and Technology, Xian University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (61571458, 61473309, 41601436)

Received Date: 2018-08-07
Rev Recd Date: 2019-01-21

Available Online: 2019-02-15

Publish Date: 2019-07-01

Abstract

Abstract

Because of the problem that the target is prone to drift in complex background, a robust tracking algorithm based on spatial reliability constraint is proposed. Firstly, the pre-trained Convolutional Neural Network (CNN) model is used to extract the multi-layer deep features of the target, and the correlation filters are respectively trained on each layer to perform weighted fusion of the obtained response maps. Then, the reliability region information of the target is extracted through the high-level feature map, a binary matrix is obtained. Finally, the obtained binary matrix is used to constrain the search area of the response map, and the maximum response value in the area is the target position. In addition, in order to deal with the long-term occlusion problem, a random selection model update strategy with the first frame template information is proposed. The experimental results show that the proposed algorithm has good performance in dealing with similar background interference, occlusion, and other scenes.
- Visual tracking,
- Spatial reliability constraint,
- Deep features,
- Correlation filter,
- Model update

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References(30)

References

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