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Stats-Calculus Pose Descriptor Feeding A Discrete HMM Low-latency Detection and Recognition System For 3D Skeletal Actions
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<span title="2015-10-16">2015</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2015-10-14">2015</span>
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<span title="2015-09-30">2015</span>
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<span title="2015-10-15">2015</span>
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Recognition of human actions, under low observational latency, is a growing interest topic, nowadays. Many approaches have been represented based on a provided set of 3D Cartesian coordinates system originated at a certain specific point located on a root joint. In this paper, We will present a statistical detection and recognition system using Hidden Markov Model using 7 types of pose descriptors. * Cartesian Calculus Pose descriptor. * Angular Calculus Pose descriptor. * Mixed-mode
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1509.09014v4">arXiv:1509.09014v4</a>
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... lus Pose descriptor. * Centro-Stats-Calculus Pose descriptor. * Rela-Centro-Stats-Calculus Pose descriptor. * Rela-Centro-Stats-Calculus DCT Pose descriptor. * Rela-Centro-Stats-Calculus DCT-AMDF Pose descriptor. Stats-Calculus is a feature extracting technique, that is developed on Moving Pose descriptor , but using a combination of Statistics measures and Calculus measures.
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