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PLS-CCA heterogeneous features fusion-based low-resolution human detection method for outdoor video surveillance

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Abstract

In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis (PLS-CCA) for outdoor video surveillance. The analysis relies on heterogeneous features fusion-based human detection method. The proposed method can not only explore the relation between two individual heterogeneous features as much as possible, but also can robustly describe the visual appearance of humans with complementary information. Compared with some other methods, the experimental results show that the proposed method is effective and has a high accuracy, precision, recall rate and area under curve (AUC) value at the same time, and offers a discriminative and stable recognition performance.

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Authors and Affiliations

  1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Hong-Kai Chen, Xiao-Guang Zhao, Shi-Ying Sun & Min Tan

Authors
  1. Hong-Kai Chen
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  2. Xiao-Guang Zhao
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  3. Shi-Ying Sun
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  4. Min Tan
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Corresponding author

Correspondence to Hong-Kai Chen.

Additional information

This works was supported by National Natural Science Foundation of China (Nos. 61271432 and 61333016).

Recommended by Associate Editor Nazim Mir-Nasiri

Hong-Kai Chen received the B. Sc. degree in control science and engineering from Xiamen University, China in 2011. He is currently a Ph. D. degree candidate at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China.

His research interests include robot vision, especially for visual tracking and target detection.

ORCID iD: 0000-0003-4932-042X

Xiao-Guang Zhao received the B. Sc. degree in control engineering from Shenyang University of Technology, China in 1992, and the M. Sc. and Ph.D. degrees in control theory and control engineering at the Shenyang Institute of Automation, Chinese Academy of Sciences, China, in 1998 and 2001, respectively. She is a professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China.

Her research interests include advanced robot control, wireless sensor network and robot vision.

Shi-Ying Sun received the B. Sc. degree in control science and engineering from Central South University, China in 2013. He is currently a Ph. D. degree candidate at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China.

His research interests include robotics and computer vision.

Min Tan received the B. Sc. degree in control engineering from Tsinghua University, China in 1986, and the Ph.D. degree in control theory and control engineering at the Institute of Automation, Chinese Academy of Sciences, China in 1990. He is a professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China.

His research interests include advanced robot control, multirobot systems, biomimetic robots, and manufacturing systems.

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Chen, HK., Zhao, XG., Sun, SY. et al. PLS-CCA heterogeneous features fusion-based low-resolution human detection method for outdoor video surveillance. Int. J. Autom. Comput. 14, 136–146 (2017). https://doi.org/10.1007/s11633-016-1029-8

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  • DOI: https://doi.org/10.1007/s11633-016-1029-8

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