Real Time Gait Recognition System Based on Kinect Skeleton Feature

详细信息    查看全文

• 作者：Shuming Jiang (15)
  Yufei Wang (16)
  Yuanyuan Zhang (15)
  Jiande Sun (16)
  
  15. Information Research Institute ; Shandong Academy of Sciences ; Jinan ; 250014 ; China
  16. School of Information Science and Engineering ; Shandong University ; Jinan ; 250100 ; China
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9008
• 期：1
• 页码：46-57
• 全文大小：1,528 KB
• 参考文献：1. Lee, L., Grimson, W.E.L.: Gait analysis for recognition and classification. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition 2002, pp. 148鈥?55. IEEE (2002)
  2. Bofeng, Z, Jingru, Z, Ke, Y (2009) Research on gait feature extracting methods based on human walking model. Comput. Appl. Softw. 26: pp. 198-201
  3. Wang, L, Tan, T, Ning, H (2003) Silhouette analysis-based gait recognition for human identification. IEEE Trans. Pattern Anal. Mach. Intell. 25: pp. 1505-1518 CrossRef
  4. Weihua, H., Ping, L., Haijun, Y.: Gait recognition using the information about Crura鈥檚 joint angle. In: 13th National Conference on Image and Graphics (NCIG 2006), pp. 411鈥?14 (2006)
  5. Bobick, AF, Davis, JW (2001) The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23: pp. 257-267 CrossRef
  6. Kale, A., Cuntoor, N., Chellappa, R.: A framework for activity-specific human identification. In: 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 4, pp. IV-3660鈥揑V-3663. IEEE (2002)
  7. Sivapalan, S., Chen, D., Denman, S., et al.: Gait energy volumes and frontal gait recognition using depth images. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1鈥?. IEEE (2011)
  8. Gabel, M., Gilad-Bachrach, R., Renshaw, E., et al.: Full body gait analysis with Kinect. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1964鈥?967. IEEE (2012)
  9. Wang, G.: Anthropometric and application. The Anthropology Department of Biology Department of Fudan University
  10. Powered by Institute of Automation, Chinese Academy of Sciences. http://www.cbsr.ia.ac.cn/china/Gait%20Databases%20CH.asp
  11. Tian, W., Cong, Q., Yan, Z., et al.: Spatio-temporal characteristics of human gaits based on joint angle analysis. In: 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 6, pp. 439鈥?42. IEEE (2010)
  12. He, W., Li, P.: Gait recognition using the temporal information of leg angles. In: 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 5, pp. 78鈥?3. IEEE (2010)
  13. Makihara, Y, Mannami, H, Yagi, Y Gait analysis of gender and age using a large-scale multi-view gait database. In: Kimmel, R, Klette, R, Sugimoto, A eds. (2011) Computer Vision 鈥?ACCV 2010. Springer, Heidelberg, pp. 440-451 CrossRef
  14. Fang, Z, Ye, W (2003) A survey on multi-biometrics. Comput. Eng. 29: pp. 140-142
  15. Wang, A.-H., Liu, J.-W.: Gait recognition method based on position humanbody joints. In: Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2鈥? November 2007
  
• 作者单位：Computer Vision - ACCV 2014 Workshops
• 丛书名：978-3-319-16627-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Gait recognition is a kind of biometric feature recognition technique, which utilizes the pose of walking to recognize the identity. Generally people analyze the normal video data to extract the gait feature. These days, some researchers take advantage of Kinect to get the depth information or the position of joints for recognition. This paper mainly focus on the length of bones namely static feature and the angles of joints namely dynamic feature based on Kinect skeleton information. After preprocessing, we stored the two kinds of feature templates into database which we established for the system. For the static feature, we calculate the distance with Euclidean distance, and we calculated the distance in dynamic time warping algorithm (DTW) for the dynamic distance. We make a feature fusion for the distance between the static and dynamic. At last, we used the nearest neighbor (NN) classifier to finish the classification, and we got a real time recognition system and a good recognition result.