Abstract
Acquisition of good quality training samples is becoming a major issue in machine learning based human motion analysis. This paper presents a method to simulate human body motion with the intention to establish a human motion corpus for learning and recognition. The simulation is achieved by a unique temporal-spatial-temporal decomposition of human body motion into actions, joint actions and actionlets based on the human kinematic model. The actionlet models the primitive moving phase of a joint and can be simulated based on the kinesiological study. A joint action is formed by proper concatenation of actionlets and an action is a group of synchronized joint actions. Methods for concatenation and synchronization are proposed in this paper for realistic simulation of human motion. Results on simulating ”running” verifies the feasibility of the proposed method.
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Zheng, G., Li, W., Ogunbona, P., Dong, L., Kharitonenko, I. (2006). Simulation of Human Motion for Learning and Recognition. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_142
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DOI: https://doi.org/10.1007/11941439_142
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
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