Visual Tracking Algorithm Based on Global Context and Feature Dimensionality Reduction

Yanjing SUN; Sainan WANG; Yunkai SHI; Xiao YUN; Wenjuan SHI

doi:10.11999/JEIT171143

Volume 40 Issue 9

Aug. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(9): 2135-2142

Yanjing SUN, Sainan WANG, Yunkai SHI, Xiao YUN, Wenjuan SHI. Visual Tracking Algorithm Based on Global Context and Feature Dimensionality Reduction[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2135-2142. doi: 10.11999/JEIT171143

Citation:

Yanjing SUN, Sainan WANG, Yunkai SHI, Xiao YUN, Wenjuan SHI. Visual Tracking Algorithm Based on Global Context and Feature Dimensionality Reduction[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2135-2142. doi: 10.11999/JEIT171143

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Visual Tracking Algorithm Based on Global Context and Feature Dimensionality Reduction

doi: 10.11999/JEIT171143

School of Information and Control Engineering, China University of Mining Technology, Xuzhou 221116, China

Funds: The Natural Science Foundation of Jiangsu Province (BK20150204), The State Key Research Development Program (2016YFC0801403), The National Natural Science Foundation of China (51504214, 51504255, 51734009, 61771417), The Research Development Programme of Jiangsu Province (BE2015040)

Received Date: 2017-12-04
Rev Recd Date: 2018-05-02

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

Abstract

Tracking effects of algorithms using correlation filter are easily interfered by deformation, motion blur and background clustering, which can result in tracking failure. To solve these problems, a visual tracking algorithm based on global context and feature dimensionality reduction is proposed. Firstly, the image patches uniformly around the target are extracted as negative sample, and thus the similar background patches around the target are suppressed. Then, an update strategy based on principal component analysis is proposed, constructing the matrix to reduce the dimensionality of HOG feature, which can reduce the redundancy of feature when it updates. Finally, the color features are added to represent the motion target and the response of the system states are adaptively fused according to the features. Experiments are performed on recent online tracking benchmark. The results show that the proposed method performs favorably both in terms of accuracy and robustness compared to the state-of-the-art trackers such as Staple or KCF. When deformation occur, the proposed method is shown to outperform the Staple tracker and KCF algorithm by 8.3% and 13.1% respectively in median distance precision.
- Visual tracking,
- Global context information,
- Feautre dimensionality reduction,
- Adaptive fusion

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References(19)

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