Abstract
With the development of Kinect sensor, action recognition based on human skeleton becomes a prosperous research field. In this paper, an improved method is proposed to select a few inconsecutive, discriminative and ordinal frames (named Improved Key Poses) to represent a human skeleton action. The main contributions of the proposed method are summarized as follow. First, a novel Key Poses Mining method is presented to keep time order of each frame in action video. Second, we selected a new feature which could reflect the micro-motion of specific actions and increase the recognition accuracy. The proposed method is evaluated on three benchmark datasets: MSR Action3D dataset, UTKinect Action dataset and Florence 3D Action dataset. The experiment results show that the proposed approach outperforms than some state-of-the-art methods.
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This work is partially supported by Shanghai Innovation Action Plan Project under the grant No. 16511101200.
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Li, X., Zhang, Y., Zhang, J. (2018). Improved Key Poses Model for Skeleton-Based Action Recognition. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_35
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DOI: https://doi.org/10.1007/978-3-319-77383-4_35
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