The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Automatic gait recognition based on statistical shape analysis
Abstract
Gait recognition has recently gained significant attention from computer vision researchers. This interest is strongly motivated by the need for automated person identification systems at a distance in visual surveillance and monitoring applications. The paper proposes a simple and efficient automatic gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of a walking figure from the background. Temporal changes of the detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain mean shape as gait signature. Supervised pattern classification techniques, based on the full Procrustes distance measure, are adopted for recognition. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the shape cues of body biometrics. The algorithm is tested on a database consisting of 240 sequences from 20 different subjects walking at 3 viewing angles in an outdoor environment. Experimental results are included to demonstrate the encouraging performance of the proposed algorithm.
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Cites
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Cited by
Robust Video Surveillance for Fall Detection Based on Human Shape Deformation
Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2011) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2011.2129370 · Cited by 490
Fusion of Static and Dynamic Body Biometrics for Gait Recognition
Wang, L., Ning, H., Tan, T., & Hu, W. (2004) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2003.821972 · Cited by 377
Self-citation(2019) · ACM Computing Surveys · doi:10.1145/3230633 · Cited by 303
Robust gait recognition: a comprehensive survey
Rida, I., Almaadeed, N., & Almaadeed, S. (2019) · IET Biometrics · doi:10.1049/iet-bmt.2018.5063 · Cited by 119
A New View-Invariant Feature for Cross-View Gait Recognition
Kusakunniran, W., Wu, Q., Zhang, J., Ma, Y., & Li, H. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2252342 · Cited by 98
Metadata
- Authors
- Wang, L., Tan, T., Hu, W., & Ning, H.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2003-09
- Volume
- 12
- Issue
- 9
- Pages
- 1120-1131
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2137474370
- Cited by
- 334
- References
- 44
- FWCI
- 8.20
- Type
- journal article
- Language
- en
- Open access
- closed
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