Identification of humans using gait

We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features,...

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Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 13(2004), 9 vom: 30. Sept., Seite 1163-73
1. Verfasser: Kale, Amit (VerfasserIn)
Weitere Verfasser: Sundaresan, Aravind, Rajagopalan, A N, Cuntoor, Naresh P, Roy-Chowdhury, Amit K, Krüger, Volker, Chellappa, Rama
Format: Aufsatz
Sprache:English
Veröffentlicht: 2004
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Comparative Study Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S. Validation Study
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520 |a We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases 
650 4 |a Comparative Study 
650 4 |a Evaluation Study 
650 4 |a Journal Article 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
650 4 |a Validation Study 
700 1 |a Sundaresan, Aravind  |e verfasserin  |4 aut 
700 1 |a Rajagopalan, A N  |e verfasserin  |4 aut 
700 1 |a Cuntoor, Naresh P  |e verfasserin  |4 aut 
700 1 |a Roy-Chowdhury, Amit K  |e verfasserin  |4 aut 
700 1 |a Krüger, Volker  |e verfasserin  |4 aut 
700 1 |a Chellappa, Rama  |e verfasserin  |4 aut 
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