Efficient action recognition via local position offset of 3D skeletal body joints

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• 作者：Guoliang Lu ; Yiqi Zhou ; Xueyong Li ; Mineichi Kudo
• 关键词：Action recognition ; Skeletal body joints ; RGB ; D data ; Bag ; of ; words
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：75
• 期：6
• 页码：3479-3494
• 全文大小：1,907 KB
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• 作者单位：Guoliang Lu (1) (2)
  Yiqi Zhou (1) (2)
  Xueyong Li (1) (2)
  Mineichi Kudo (3)
  
  1. School of Mechanical Engineering, Shandong University, Jinan, China
  2. Key Laboratory of High-efficiency and Clean Mechanical Manufacture of MOE, Shandong University, Jinan, China
  3. Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

To accurately recognize human actions in less computational time is one important aspect for practical usage. This paper presents an efficient framework for recognizing actions by a RGB-D camera. The novel action patterns in the framework are extracted via computing position offset of 3D skeletal body joints locally in the temporal extent of video. Action recognition is then performed by assembling these offset vectors using a bag-of-words framework and also by considering the spatial independence of body joints. We conducted extensive experiments on two benchmarking datasets: UCF dataset and MSRC-12 dataset, to demonstrate the effectiveness of the proposed framework. Experimental results suggest that the proposed framework 1) is very fast to extract action patterns and very simple in implementation; and 2) can achieve a comparable or a better performance in recognition accuracy compared with the state-of-the-art approaches.