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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2018> 27(4): 592-603
Gensheng Hu, Min Li, Dong Liang, Mingzhu Wan, Wenxia Bao. Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(4): 592-603. doi: 10.15918/j.jbit1004-0579.17120
Citation: Gensheng Hu, Min Li, Dong Liang, Mingzhu Wan, Wenxia Bao. Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2018, 27(4): 592-603.doi:10.15918/j.jbit1004-0579.17120
shu

Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning

doi:10.15918/j.jbit1004-0579.17120
  • 1.

    Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China;School of Electronics and Information Engineering, Anhui University, Hefei 230601, China;Anhui Key Laboratory of Polarization Imaging Detection Technology, Hefei 230031, China

  • 2.

    Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China;School of Electronics and Information Engineering, Anhui University, Hefei 230601, China

  • 3.

    School of Information Science and Technology, Fudan University, Shanghai 200433, China

  • Received Date:2017-11-02
    Abstract
  • A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning. Non-sampled dual-tree complex wavelet packet transform (NS-DTCWPT) is used to decompose the human images in videos into multi-scale and multi-resolution. An improved local binary pattern (ILBP) and an inner-distance shape context (IDSC) combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features. The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem. The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning. Experimental results on behave video set, group activity video set, and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.
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