HMM-based gait modeling and recognition under different walking scenarios

Abstract : This paper addresses gait recognition, the problem of identifying people by the way of their walk. The proposed system consists of a model-free approach which extracts features directly from the human silhouette. The dynamics of the gait are modeled using Hidden Markov Models. Experiments have been carried out on the CASIA dataset C consisting of 153 people under four walking scenarios: normal walking, slow walking, fast walking and walking while carrying a bag. The results obtained are promising and compare favorably with existing approaches
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Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Thursday, April 14, 2016 - 1:53:02 PM
Last modification on : Tuesday, January 22, 2019 - 2:32:09 PM



Mounim El Yacoubi, Ayet Shaiek, Bernadette Dorizzi. HMM-based gait modeling and recognition under different walking scenarios. ICMCS 2011 : 2nd International Conference on Multimedia Computing and Systems, Apr 2011, Ouarzazate Morocco. pp.1 - 5, ⟨10.1109/ICMCS.2011.5945573⟩. ⟨hal-01302472⟩



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