Abstract
This paper proposes one novel on-line gesture segmentation and recognition method for one-shot-learning on depth video. In each depth image, we take several random points from the motion region and select a group of relevant points for each random point. Depth difference between each random point and its relevant points is calculated in Motion History Images. The results are used to generate the random point’s feature. Then we use Random Decision Forest to assign gesture label to each random point and work out the probability distribution vector (PDV) for each frame in the video. Finally, we gain a probability distribution matrix (PDM) using PDVs of sequential frames and do on-line segmentation and recognition for one-shot-learning. Experimental results show our method is competitive to the state-of-the- art methods.
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Rong, T., Yang, R. (2016). One-Shot-Learning Gesture Segmentation and Recognition Using Frame-Based PDV Features. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_35
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DOI: https://doi.org/10.1007/978-3-319-48890-5_35
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