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Human motion analysis: a review
Abstract
Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man-machine interfaces, content-based image storage and retrieval, and video conferencing. The paper gives an overview of the various tasks involved in motion analysis of the human body. The authors focus on three major areas related to interpreting human motion: 1) motion analysis involving human body parts, 2) tracking of human motion using single or multiple cameras, and 3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints, and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion using a single or multiple camera focuses on higher-level processing, in which moving humans are tracked without identifying specific parts of the body structure. After successfully matching the moving human image from one frame to another in image sequences, understanding the human movements or activities comes naturally, which leads to a discussion of recognizing human activities. The review is illustrated by examples.
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Cites
Recognizing human action in time-sequential images using hidden Markov model
Yamato, J., Ohya, J., & Ishii, K. (2003) · Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1992.223161 · Cited by 1,273
Research works printed but not linked: 50
- 10.1109/TPAMI.1980.6447705 · DOI
- 10.1109/ICASSP.1988.196495 · DOI
- 10.1109/CVPR.1994.323868 · DOI · Paper page
- 10.1098/rspb.1978.0020 · DOI · Paper page
- 10.1109/34.385981 · DOI
- 10.1109/ICPR.1992.201607 · DOI
- 10.1109/MNRAO.1994.346251 · DOI
- 10.1109/MNRAO.1994.346263 · DOI
- 10.1109/TPAMI.1980.6447699 · DOI · Paper page
- 10.1109/IROS.1992.601989 · DOI
- kinzel, 1994, Pedestrian recognition by modelling their shapes and movements, in progress in image analysis and processing III, Proc of the 7th Intl Conf on Image Analysis and Processing 1993, 547
- 10.1145/217279.215267 · DOI
- 10.1109/ICPR.1996.547291 · DOI
- 10.1109/PROC.1981.12025 · DOI
- aggarwal, 0, Non-rigid motion analysis: Articulated & elastic motion, CVGIP Image Understanding
- 10.1109/ICCV.1995.466874 · DOI
- 10.1109/MMCS.1995.484925 · DOI
- 10.1109/TPAMI.1979.4766907 · DOI
- 10.1109/CVPR.1996.517057 · DOI
- 10.1038/scientificamerican0675-76 · DOI · Paper page
- 10.1109/CVPR.1994.323938 · DOI
- 10.1109/ICCV.1995.466881 · DOI
- 10.1109/ACV.1996.572043 · DOI · Paper page
- 10.1109/ICIP.1995.529584 · DOI
- 10.1109/ICCV.1995.466880 · DOI · Paper page
- 10.1109/ICCV.1995.466882 · DOI · Paper page
- 10.1109/21.148408 · DOI
- 10.1016/0167-2789(91)90222-U · DOI · Paper page
- 10.1109/ACV.1996.572010 · DOI
- 10.1109/MNRAO.1994.346250 · DOI
- 10.1109/ICCV.1995.466861 · DOI
- 10.1016/0262-8856(83)90003-3 · DOI · Paper page
- 10.1016/0004-3702(81)90024-2 · DOI · Paper page
- 10.1109/ACV.1996.572020 · DOI
- 10.1016/0031-3203(84)90036-0 · DOI
- 10.1109/5.5965 · DOI · Paper page
- 10.1109/MNRAO.1994.346252 · DOI
- 10.1109/ICPR.1996.547022 · DOI
- 10.1109/ICCV.1995.466914 · DOI
- 10.1109/ACV.1996.571995 · DOI
- wren, 1995, Pfinder: Real-time tracking of the human body, Proc SPIE
- 10.1109/ICPR.1996.546796 · DOI
- starner, 1995, Visual recognition of American Sign Language using Hidden Markov Models, Proc Intl Workshop on Automatic Face and Gesture Recognition
- 10.1109/WVM.1991.212768 · DOI
- 10.1016/0004-3702(82)90023-6 · DOI
- 10.1109/C-M.1981.220561 · DOI
- rossi, 1994, Tracking and counting people, IEEE Int Conf Image Processing, 212, 10.1109/ICIP.1994.413857 · DOI
- 10.1006/ciun.1994.1006 · DOI · Paper page
- 10.1109/ICPR.1996.546795 · DOI
- 10.1109/CADVIS.1994.284490 · DOI
Cited by
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
A survey of socially interactive robots
Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(02)00372-x · Cited by 3,162
A survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242
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
Sensor-Based Activity Recognition
Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2198883 · Cited by 1,074
Multimodal human–computer interaction: A survey
Jaimes, A., & Sebe, N. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.10.019 · Cited by 965
Neural mechanisms for the recognition of biological movements
Giese, M. A., & Poggio, T. (2003) · Nature Reviews Neuroscience · doi:10.1038/nrn1057 · Cited by 955
Human motion tracking for rehabilitation-A survey
Zhou, H., & Hu, H. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.09.001 · Cited by 813
A survey of video datasets for human action and activity recognition
Chaquet, J. M., Carmona, E. J., & Fernández-Caballero, A. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.01.013 · Cited by 455
Object relevance weight pattern mining for activity recognition and segmentation
Palmes, P., Pung, H. K., Gu, T., Xue, W., & Chen, S. (2010) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2009.10.004 · Cited by 112
Efficient duration and hierarchical modeling for human activity recognition
Duong, T., Phung, D., Bui, H., & Venkatesh, S. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2008.12.005 · Cited by 115
Han, J., Pauwels, E. J., de Zeeuw, P. M., & de With, P. H. N. (2012) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2012.6227420 · Cited by 104
Multimodal interfaces: Challenges and perspectives
Sebe, N. (2009) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-2009-0003 · Cited by 57
Multiple Cameras Using Real Time Object Tracking for Surveillance and Security System
Susheel Kumar, K., Prasad, S., Saroj, P. K., & Tripathi, R. C. (2010) · 2010 3rd International Conference on Emerging Trends in Engineering and Technology · doi:10.1109/icetet.2010.30 · Cited by 32
Rogers, W. A., Essa, I. A., & Fisk, A. D. (2007) · Ergonomics in Design: The Quarterly of Human Factors Applications · doi:10.1177/106480460701500303 · Cited by 12
Metadata
- Authors
- Aggarwal, J. K., & Cai, Q.
- Venue
- Proceedings IEEE Nonrigid and Articulated Motion Workshop
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-22
- Pages
- 90-102
- OpenAlex
- W2113856781
- Cited by
- 1,400
- References
- 1
- FWCI
- 53.69
- Type
- conference paper
- Language
- en
- Open access
- closed
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