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Human action recognition based on scene semantics

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Abstract

Like outdoors, indoor security is also a critical problem and human action recognition in indoor area is still a hot topic. Most studies on human action recognition ignored the semantic information of a scene, whereas indoors contains varieties of semantics. Meanwhile, the depth sensor with color and depth data is more suitable for extracting the semantics context in human actions. Hence, this paper proposed an indoor action recognition method using Kinect based on the semantics of a scene. First, we proposed a trajectory clustering algorithm for a three-dimensional (3D) scene by combining the different characteristics of people such as the spatial location, movement direction, and speed. Based on the clustering results and scene context, it concludes a region of interest (ROI) extraction method for indoors, and dynamic time warping (DTW) is used to study the abnormal action sequences. Finally, the color and depth-data-based 3D motion history image (3D–MHI) features and the semantics context of the scene were combined to recognize human action. In the experiment, two datasets were tested and the results demonstrate that our semantics-based method performs better than other methods.

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Acknowledgements

This research is supported by the National Key Research and Development Program of China (No. 2016YFB0502204), the National Natural Science Foundation of China (No. 41301517), the Funds for the Central Universities (No. 413000010), the Open Found of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University (No.16(03)), the National Key Technology Research and Development Program (No. 2012BAH35B03).

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Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430070, China

    Tao Hu, Xinyan Zhu & Wei Guo

  2. Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, 430070, China

    Tao Hu, Xinyan Zhu & Wei Guo

  3. School of Resource and Environment Sciences, Wuhan University, Wuhan, 430070, China

    Tao Hu

  4. School of Information, Kent State University, Kent, OH, 44240, USA

    Tao Hu

  5. International School of Software, Wuhan University, Wuhan, 430070, China

    Shaohua Wang

  6. School of Digital Sciences, Kent State University, Kent, OH, 44240, USA

    Jianfeng Zhu

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  1. Tao Hu
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  2. Xinyan Zhu
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  3. Wei Guo
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  4. Shaohua Wang
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  5. Jianfeng Zhu
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Correspondence to Wei Guo.

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Hu, T., Zhu, X., Guo, W. et al. Human action recognition based on scene semantics. Multimed Tools Appl 78, 28515–28536 (2019). https://doi.org/10.1007/s11042-017-5496-x

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  • DOI: https://doi.org/10.1007/s11042-017-5496-x

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