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Exploring hybrid spatio-temporal convolutional networks for human action recognition

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Abstract

Convolutional neural networks have achieved great success in many computer vision tasks. However, it is still challenging for action recognition in videos due to the intrinsically complicated space-time correlation and computational difficult of videos. Existing methods usually neglect the fusion of long term spatio-temporal information. In this paper, we propose a novel hybrid spatio-temporal convolutional network for action recognition. Specifically, we integrate three different type of streams into the network: (1) the image stream utilizes still images to learn the appearance information; (2) the optical stream captures the motion information from optical flow frames; (3) the dynamic image stream explores the appearance information and motion information simultaneously from generated dynamic images. Finally, a weighted fusion strategy at the softmax layer is utilized to make the class decision. With the help of these three streams, we can take full advantage of the spatio-temporal information of the videos. Extensive experiments on two popular human action recognition datasets demonstrate the superiority of our proposed method when compared with several state-of-the-art approaches.

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Acknowledgements

The authors would like to thank the Editor-in-Chief, the handling associate editor and all anonymous reviewers for their considerations and suggestions. This work was supported by the National Natural Science Foundation of China (61572388).

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Authors and Affiliations

  1. Department of Electronic and Engineering, Xidian University, Xi’an, 710071, China

    Hao Wang, Yanhua Yang, Erkun Yang & Cheng Deng

  2. The State Key Laboratory of Integrated Services Networks (ISN), Xidian University, Xi’an, 710071, China

    Cheng Deng

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  1. Hao Wang
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  2. Yanhua Yang
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Correspondence to Cheng Deng.

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Wang, H., Yang, Y., Yang, E. et al. Exploring hybrid spatio-temporal convolutional networks for human action recognition. Multimed Tools Appl 76, 15065–15081 (2017). https://doi.org/10.1007/s11042-017-4514-3

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