Abstract
We concentrate on recognizing persons according to the way they walk. Our approach considers a human movement as a set of trajectories of hips, knees, and feet captured as the person walks. The trajectories are used for the extraction of viewpoint invariant planar signals that express how a distance between a pair of specific points on the human body changes in time. We solely focus on analysis and normalization of extracted signals to simplify their similarity comparison, without presenting any specific gait recognition method. In particular, we propose a novel method for automatic determination of walk cycles within extracted signals and evaluate its importance on a real-life human motion database.
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Valcik, J., Sedmidubsky, J., Balazia, M., Zezula, P. (2012). Identifying Walk Cycles for Human Recognition. In: Chau, M., Wang, G.A., Yue, W.T., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2012. Lecture Notes in Computer Science, vol 7299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30428-6_10
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DOI: https://doi.org/10.1007/978-3-642-30428-6_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30427-9
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