Hong-Kai Chen, Xiao-Guang Zhao, Shi-Ying Sun and Min Tan. PLS-CCA Heterogeneous Features Fusion-based Low-resolution Human Detection Method for Outdoor Video Surveillance. International Journal of Automation and Computing, vol. 14, no. 2, pp. 136-146, 2017. DOI: 10.1007/s11633-016-1029-8
Citation: Hong-Kai Chen, Xiao-Guang Zhao, Shi-Ying Sun and Min Tan. PLS-CCA Heterogeneous Features Fusion-based Low-resolution Human Detection Method for Outdoor Video Surveillance. International Journal of Automation and Computing, vol. 14, no. 2, pp. 136-146, 2017. DOI: 10.1007/s11633-016-1029-8

PLS-CCA Heterogeneous Features Fusion-based Low-resolution Human Detection Method for Outdoor Video Surveillance

  • 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.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return