Abstract
Human action recognition using depth cameras is an important and challenging task which can involve highly similar motions in different actions. In addition, another factor which makes the problem difficult, is the large amount of intra class variations within the same action class. In this paper, we explore a Dynamic Frame Warping framework as an extension to the Dynamic Time Warping framework from the RGB domain, to address the action recognition with depth cameras. We employ intuitively relevant skeleton joints based features from the depth stream data generated using Microsoft Kinect. We show that the proposed approach is able to generate better accuracy for cross-subject evaluation compared to state-of-the-art works even on complex actions as well as simpler actions but which are similar to each other.
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Gupta, K., Bhavsar, A. (2017). Human Action Recognition for Depth Cameras via Dynamic Frame Warping. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_12
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DOI: https://doi.org/10.1007/978-981-10-2107-7_12
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