Abstract
In this paper, a new effective method was proposed to recognize human actions based on RGBD data sensed by a depth camera, namely Microsoft Kinect. Skeleton data extracted from depth images was utilized to generate 10 direction features which represent specific body parts and 11 position features which represent specific human joints. The fusion features composed of both was used to represent a human posture. An algorithm based on the difference level of adjacent postures was presented to select the key postures from an action. Finally, the action features, composed of the key postures’ features, were classified and recognized by a multiclass Support Vector Machine. Our major contributions are proposing a new framework to recognize the users’ actions and a simple and effective method to select the key postures. The recognition results in the KARD dataset and the Florence 3D Action dataset show that our approach significantly outperforms the compared methods.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China under Grant No. 61403302 and the Fundamental Research Funds for the Central Universities No. XJJ2016029.
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Ling, J., Tian, L., Li, C. (2016). 3D Human Activity Recognition Using Skeletal Data from RGBD Sensors. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_14
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DOI: https://doi.org/10.1007/978-3-319-50832-0_14
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