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Locating and recognizing multiple human actions by searching for maximum score subsequences

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Abstract

Despite the numerous methods to recognize human actions in a video, few are designed for videos containing more than one action over a certain time period. Moreover, existing multiple action recognition methods adopt windowed sequence search strategy. Windowed sequence searching requires an exhaustive trial of window length yielding intensive computation. This work presents a frame-based strategy, capable of searching for maximum score subsequences that correspond to actions. Therefore, start and ending times of all actions are located, and action categories are identified as well. Moreover, contrast mutual information is proposed as a new score function to increase recognition accuracy. Experimental results indicate that the proposed method locates and recognizes multiple actions in a video accurately, even for the conventional single action classification problem.

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Acknowledgments

The authors would like to thank the anonymous reviewers for the valuable and insightful comments on the earlier version of this manuscript. This work was partially supported by National Nature Science Foundation of China (No. 61202143), the Nature Science Foundation of Fujian Province (No. 2011J01367), Xiamen University 985 Project and National Science Council of Taiwan (NSC-101-2221-E-155-060).

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Authors and Affiliations

  1. School of Information Science and Technology, Xiamen University, Xiamen, China

    Hong-Bo Zhang, Shao-Zi Li, Song-Zhi Su, Xian-Ming Lin & Dong-Lin Cao

  2. Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen University, Xiamen, China

    Hong-Bo Zhang, Shao-Zi Li, Song-Zhi Su, Xian-Ming Lin & Dong-Lin Cao

  3. Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan

    Shu-Yuan Chen

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  1. Hong-Bo Zhang
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  2. Shao-Zi Li
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  3. Shu-Yuan Chen
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  4. Song-Zhi Su
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  5. Xian-Ming Lin
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Correspondence to Shao-Zi Li or Song-Zhi Su.

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Zhang, HB., Li, SZ., Chen, SY. et al. Locating and recognizing multiple human actions by searching for maximum score subsequences. SIViP 9, 705–714 (2015). https://doi.org/10.1007/s11760-013-0501-y

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  • DOI: https://doi.org/10.1007/s11760-013-0501-y

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