Human Action Recognition Using a Depth Sequence Key-frames Based on Discriminative Collaborative Representation Classifier for Healthcare Analytics

Yuhang Wang1, Tao Feng2, 3 and Yi Zheng1, 4

  1. Institute of physical culture, Harbin University
    Harbin 150000, China
    83008943@qq.com, 86959936@qq.com
  2. Department of Physical Education, Harbin Finance University
    Harbin,150000, China
    ancrum@qq.com
  3. The Graduate School of Saint Paul University Philippines
    Ottawa, Philippines

Abstract

Using deep map sequence to recognize human action is an important research field in computer vision. The traditional deep map-based methods have a lot of redundant information. Therefore, this paper proposes a new deep map sequence feature expression method based on discriminative collaborative representation classifier, which highlights the time sequence of human action features. In this paper, the energy field is established according to the shape and action characteristics of human body to obtain the energy information of human body. Then the energy information is projected onto three orthogonal axes to obtain deep spatialtemporal energy map. Meanwhile, in order to solve the problem of high misclassification probability of similar samples by collaborative representation classifier (CRC), a discriminative CRC (DCRC) is proposed. The classifier takes into account the influence of all training samples and each kind of samples on the collaborative representation coefficient, it obtains the highly discriminative collaborative representation coefficient, and improves the discriminability of similar samples. Experimental results on MSR Action3D data set show that the redundancy of key-frame algorithm is reduced, and the operation efficiency of each algorithm is improved by 20%-30%. The proposed algorithm in this paper reduces the redundant information in deep map sequence and improves the extraction rate of feature map. It not only preserves the spatial information of human action through the energy field, but also records the temporal information of human action in a complete way. What’s more, it still maintains a high recognition accuracy in the action data with temporal information.

Key words

action recognition, deep map sequence, deep spatial-temporal energy map, discriminative CRC, energy information

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS210322042W

Publication information

Volume 19, Issue 3 (September 2022)
Year of Publication: 2022
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wang, Y., Feng, T., Zheng, Y.: Human Action Recognition Using a Depth Sequence Key-frames Based on Discriminative Collaborative Representation Classifier for Healthcare Analytics. Computer Science and Information Systems, Vol. 19, No. 3, 1445-1462. (2022), https://doi.org/10.2298/CSIS210322042W