Abstract
A virtual particle random walking theory under variable velocities is presented in this paper. Under the proposed theory, the solutions of some two-dimensional Poisson equations, which are discretized by nine-point finite difference method and defined on the so-called spatial-temporal motion accumulative image stemming from human silhouettes, provide us the depth contour image for actions description. Although merely two-dimensional definition domain and concepts are related to the Poisson equations, both spatial and temporal evolution information of human actions are successfully included in the depth contour image owing to designating the travelling velocities of virtual particles according to the spatial-temporal motion accumulative image. In addition, it is worth noting that projecting three-dimensional human actions to the two-dimensional image descriptors contributes to much lower computation cost in the corresponding recognition algorithms, compared to those when using the three-dimensional spatial-temporal descriptors directly. In order to enhance the recognition accuracy, a hierarchical cascaded classifier is configured with cascading nearest neighbor classifiers, in each layer of which different kinds of shape and kinematic features of human actions are dealt with. Numerical experimental results on several public human action databases are illustrated to verify recognition performance improvements by means of the proposed algorithm.
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The authors would like to thank the Nature Science Foundation of Jiangsu Province, China, under Grant No. BK20140860, and the National Natural Science Foundation (NNSF) of China under Grant No. 61573001
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Qian, H., Zhou, J., Mao, Y. et al. Recognizing human actions from silhouettes described with weighted distance metric and kinematics. Multimed Tools Appl 76, 21889–21910 (2017). https://doi.org/10.1007/s11042-017-4610-4
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DOI: https://doi.org/10.1007/s11042-017-4610-4