Browse > Article
http://dx.doi.org/10.9717/kmms.2020.23.8.927

Human Gait Recognition Based on Spatio-Temporal Deep Convolutional Neural Network for Identification  

Zhang, Ning (Dept. of Information and Communication Engineering, Tongmyong University)
Park, Jin-ho (Dept. of Information and Communication Engineering, Tongmyong University)
Lee, Eung-Joo (Dept. of Information and Communication Engineering, Tongmyong University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.8, 2020 , pp. 927-939 More about this Journal
Abstract
Gait recognition can identify people's identity from a long distance, which is very important for improving the intelligence of the monitoring system. Among many human features, gait features have the advantages of being remotely available, robust, and secure. Traditional gait feature extraction, affected by the development of behavior recognition, can only rely on manual feature extraction, which cannot meet the needs of fine gait recognition. The emergence of deep convolutional neural networks has made researchers get rid of complex feature design engineering, and can automatically learn available features through data, which has been widely used. In this paper,conduct feature metric learning in the three-dimensional space by combining the three-dimensional convolution features of the gait sequence and the Siamese structure. This method can capture the information of spatial dimension and time dimension from the continuous periodic gait sequence, and further improve the accuracy and practicability of gait recognition.
Keywords
Video; Gait Recognition; Identification; Siamese Neural Network; Convolutional Neural Network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 F. Huiyuan, Research on Intelligent Video Scene Understanding Technology for Crowd Supervision, Master's Thesis of Beijing University of Posts and Telecommunications, 2014.
2 M. Huadong, Z. Chengbin, and X.L. Charles, “A Reliable People Counting System Via Multiple Cameras,” ACM Transactions on Intelligent Systems and Technology, Vol. 3, No. 2, pp. 1-22, 2012.
3 F. Huiyuan, M. Huadong, "Real-time Crowd Detection Based on Gradient Magnitude Entropy Model," Proceeding of the ACM International Conference on Multimedia, pp. 885-888, 2014.
4 N. Zhang and E.J. Lee, “Human Activity Recognition Based on an Improved Combined Feature Representation,” Journal of Korea Multimedia Society, Vol. 21, No. 12, pp. 1473-1480, 2019.   DOI
5 H. Kaiqi, C. Xiaotang, K. Yunfeng, and T. Tieniu, “Overview of Intelligent Video Surveillance Technology,” Journal of Computer , Vol. 37, No. 49, pp. 1093-1118, 2015.
6 Gait(2020), https://en.wikipedia.org/wiki/Gait
7 G.W. Taylor, R. Fergus, Y. Lecun, and C. Bregler, "Convolutional Learning of Spatiotemporal Features," Proceeding of European Conference on Computer Vision, pp. 140-153, 2010.
8 D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, "Learning Spatiotemporal Features with 3D Convolutional Networks," Proceeding of IEEE International Conference on Computer Vision, pp. 4489-4497, 2015.
9 A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L.F. Fei, "Large-scale Video Classification with Convolutional Neural Networks," Proceeding of IEEE Conference Computer Vision and Pattern Recognition, pp. 1725-1732, 2014.
10 K. Simonyan and A. Zisserman, "Two-stream Convolutional Networks for Action Recognition in Videos," Proceeding of Advances in Neural Information Processing Systems, pp. 568-576, 2014.
11 D. Muramatsu, A. Shiraishi, Y. Makihara, M.Z. Uddin, and Y. Yagi, “Gait-based Person Recognition Using Arbitrary View Transformation Model,” IEEE Transactions on Image Processing, Vol. 24, No. 1, pp. 140-154, 2015.   DOI
12 J. Yangqing, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, et al., "Caffe: Convolutional Architecture for Fast Feature Embedding," Proceeding of ACM International Conference on Multimedia, pp. 675-678, 2014.
13 H. Iwama, M. Okumura, Y. Makihara, and Y. Yagi, “The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition,” IEEE Transactions on Information Forensics and Security, Vol. 7, No. 5, pp. 1511-1521, 2012.   DOI
14 S. Sivapalan, D. Chen, S. Denman, S. Sridharan, and C. Fookes, "Histogram of Weighted Local Directions for Gait Recognition," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 125-130, 2013.
15 R.M. Fele and T. Xiang, "Gait Recognition by Ranking," Proceeding of European Conference on Computer Vision, pp. 328-341, 2012.
16 Z. Cheng, L. Wu, M. Huadong, and F. Huiyuan, "Siamese Neural Network Based Gait Recognition for Human Identification," Proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2832-2836, 2016.
17 H. Tiejun, Z. Jin, L. Bo, F. Huiyuan, M. Huadong, X. Xiangyang, et al., “Multimedia Technology Research: 2013-visual Perception and Processing for Intelligent Video Surveillance,” Journal of Chinese Image and Graphics, Vol. 19, No. 11, pp. 1539-1562, 2014.
18 M. Huadong, “Internet of Things: Objectives and Scientific Challenges,” Journal of Computer Science and Technology, Vol. 26, No. 6, pp. 919-924, 2011.   DOI