Primitive Human Action Recognition Based on Partitioned Silhouette Block Matching

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• 作者：Toru Abe ; Masaru Fukushi ; Daisuke Ueda
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8034
• 期：1
• 页码：318-327
• 全文大小：297KB
• 参考文献：1. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Computer Vision and Image Understanding?81, 231-68 (2001) CrossRef
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  3. Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities: A survey. IEEE Trans. Circuits Syst. Video Technol.?18, 1473-488 (2008) CrossRef
  4. Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing?28, 976-90 (2010) CrossRef
  5. Wang, L., Suter, D.: Learning and matching of dynamic shape manifolds for human action recognition. IEEE Trans. Image Process.?16, 1646-661 (2007) CrossRef
  6. Weinland, D., Boyer, E.: Action recognition using exemplar-based embedding. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1- (2008)
  7. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes (2007), http://www.wisdom.weizmann.ac.il/~vision/SpaceTime-Actions.html (online; accessed May 1, 2013)
  
• 作者单位：Toru Abe (28)
  Masaru Fukushi (29)
  Daisuke Ueda (30)
  
  28. Tohoku University, Katahira 2-1-1, Aoba-ku, Sendai, 980-8577, Japan
  29. Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, 755-8611, Japan
  30. SAXA, Inc., Miyashimo 3-14-15, Chuo-ku, Sagamihara, 252-5221, Japan
  
• ISSN：1611-3349

文摘

This paper deals with the issue of recognizing primitive human actions through template matching with time series silhouette images. Although existing methods based on this simple approach can recognize a subject’s action from a low-resolution image sequence, which is a basic requirement for surveillance applications, their recognition accuracy decreases considerably for corrupted silhouettes due to occlusion. To deal with this problem while keeping algorithm simplicity, we propose a novel method, which integrates template matching results for temporally and spatially partitioned silhouette blocks. Experimental results indicate that our method outperforms the existing methods in the accuracy of action recognition for corrupted silhouettes.