The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Gait analysis for recognition and classification
Abstract
We describe a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple features such as moments extracted from orthogonal view video silhouettes of human walking motion. Despite its simplicity, the resulting feature vector contains enough information to perform well on human identification and gender classification tasks. We explore the recognition behaviors of two different methods to aggregate features over time under different recognition tasks. We demonstrate the accuracy of recognition using gait video sequences collected over different days and times and under varying lighting environments. In addition, we show results for gender classification based our gait appearance features using a support-vector machine.
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Citations
Cites
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) · doi:10.1109/cvpr.1999.784637 · Cited by 6,923
Self-citationThe recognition of human movement using temporal templates
Bobick, A. F., & Davis, J. W. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.910878 · Cited by 2,811
Recognizing friends by their walk: Gait perception without familiarity cues
Cutting, J. E., & Kozlowski, L. T. (1977) · Bulletin of the Psychonomic Society · doi:10.3758/bf03337021 · Cited by 1,075
Johansson, G. (1975) · Scientific American · doi:10.1038/scientificamerican0675-76 · Cited by 939
Recognizing the sex of a walker from a dynamic point-light display
Kozlowski, L. T., & Cutting, J. E. (1977) · Perception & Psychophysics · doi:10.3758/bf03198740 · Cited by 856
Tracking people with twists and exponential maps
Bregler, C., & Malik, J. (2002) · Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231) · doi:10.1109/cvpr.1998.698581 · Cited by 719
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
Sidenbladh, H., Black, M. J., & Fleet, D. J. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45053-x_45 · Cited by 700
Robust real-time periodic motion detection, analysis, and applications
Cutler, R., & Davis, L. S. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868681 · Cited by 696
The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rauss, P., & Rizvi, S. A. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609311 · Cited by 590
(2001) · Cited by 555
Cited by
The humanID gait challenge problem: data sets, performance, and analysis
Sarkar, S., Phillips, P. J., Liu, Z., Vega, I. R., Grother, P., & Bowyer, K. W. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.39 · Cited by 1,133
Social signal processing: Survey of an emerging domain
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.11.007 · Cited by 842
Gait Partitioning Methods: A Systematic Review
Taborri, J., Palermo, E., Rossi, S., & Cappa, P. (2016) · Sensors · doi:10.3390/s16010066 · Cited by 361
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
Zou, Q., Wang, Y., Wang, Q., Zhao, Y., & Li, Q. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2985628 · Cited by 305
(2019) · ACM Computing Surveys · doi:10.1145/3230633 · Cited by 303
Metadata
- Authors
- Lee, L., & Grimson, W. E. L.
- Venue
- Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition
- Publisher
- IEEE
- Published
- 2003-06-25
- Pages
- 155-162
- OpenAlex
- W2135541996
- Cited by
- 734
- References
- 15
- FWCI
- 58.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
