Abstract
A local feature descriptor based on energy information is presented which combines kinetic energy, potential energy and the position information of 3D skeleton joints etc. These features conform to not only kinematics and biology of human action, but also the natural visual saliency for action recognition. The semantic features is obtained by the bag of word (BOW) based on k-means clustering. Finally, SVM based on kernel function is used to carry out human activity recognition. The experimental results show that the accuracy of human activity recognition based on low dimensional features is higher than several state-of-the-art algorithms.
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Acknowledgments
Supported by the National Natural Science Foundation of China under Grant 61374039, 61403254, the Hujiang Foundation of China (C14002, B1402/D1402, D15009).
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Shi, Y., Wang, Y. (2015). A Local Feature Descriptor Based on Energy Information for Human Activity Recognition. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_34
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DOI: https://doi.org/10.1007/978-3-319-22053-6_34
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