The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Model based extraction of articulated objects in image sequences for gait analysis
Abstract
This paper describes an approach to the extraction of articulated objects which will be used for gait analysis. In most medical applications markers are used to determine trajectories of different body parts. This approach works without any markers. Monotony operators which compute the displacement vector field are used to initialize a contour based tracking algorithm called active rays-for several body parts which are important for gait analysis. The contours of different parts of the human body are extracted and tracked. These parts are approached by simple 3D geometric objects (blocks), which 3D position and motion are estimated for the each image of the image sequence. Then, the trajectories of the moving parts represented by the 3D blocks can be determined and used for classification of different gait disorders.
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“Traditional detectors, such as contour-based target detection [29], the Harris corner detector [30], SIFT, and feature point-based methods [31,32], were used to produce false detection that resulted further in false matching.”
From Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network (2022) · cites this paper as [29]
Citations
Cites
Towards Model-Based Recognition of Human Movements in Image Sequences
Rohr, K. (1994) · CVGIP Image Understanding · doi:10.1006/ciun.1994.1006 · Cited by 384
First Sight: A human body outline labeling system
Leung, M. K. H., & Yang, Y. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.385981 · Cited by 170
3D human body model acquisition from multiple views
Kakadiaris, I. A., & Metaxas, D. (2002) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466881 · Cited by 167
Cyclic motion detection for motion based recognition
Tsai, P.-S., Shah, M. A., Keiter, K., & Kasparis, T. (1994) · Pattern Recognition · doi:10.1016/0031-3203(94)90079-5 · Cited by 96
Model-based object tracking in traffic scenes
Koller, D., Daniilidis, K., Thórhallson, T., & Nagel, H.-H. (1992) · Lecture notes in computer science · doi:10.1007/3-540-55426-2_49 · Cited by 78
Describing motion for recognition
Little, J. J., & Boyd, J. E. (2002) · International Symposium on Computer Vision · doi:10.1109/iscv.1995.477007 · Cited by 66
Polana, R., & Nelson, R. C. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1994.576454 · Cited by 36
Active Rays: A New Approach to Contour Tracking
Denzler, J., Niemann, H., Lehrstuhl, F., April, L., Denzler, J., Niemann, H., & Lehrstuhl, F. (1996) · Journal of Computing and Information Technology · Cited by 11
Self-citation3D data driven prediction for active contour models based on geometric bounding volumes
Denzler, J., & Niemann, H. (1996) · Pattern Recognition Letters · doi:10.1016/0167-8655(96)00072-4 · Cited by 6
Self-citation
Research works printed but not linked: 3
- denzler, 1996, Active rays: A new approach to contour tracking, International Journal of Computer Science and Information Technology, 4, 9
- gavrila, 1996, Tracking of humans in action: A 3D model-based approach, ARPA Image Understanding Workshop
- davis, 1996, Appearence-based motion recognition of human actions
Cited by
Sorted by citations
A Survey on Visual Surveillance of Object Motion and Behaviors
Hu, W., Tan, T. N., Wang, L., & Maybank, S. J. (2004) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2004.829274 · Cited by 2,156
A Survey of Computer Vision-Based Human Motion Capture
Moeslund, T. B., & Granum, E. (2001) · Computer Vision and Image Understanding · doi:10.1006/cviu.2000.0897 · Cited by 1,788
Recent developments in human motion analysis
Wang, L., Hu, W., & Tan, T. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00100-0 · Cited by 971
3D Gait Recognition Using Multiple Cameras
Zhao, G., Liu, G., Li, H., & Pietikäinen, M. (2006) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fgr.2006.2 · Cited by 197
Nixon, M. S. (1999) · doi:10.1049/ic:19990573 · Cited by 147
Metadata
- Authors
- Meyer, D., Denzler, J., & Niemann, H.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- ORCID
- Niemann, H.
- Venue
- Proceedings of International Conference on Image Processing
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-23
- Volume
- 3
- Pages
- 78-81
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2117490925
- Cited by
- 115
- References
- 9
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 2 · 2022: 1 · 2021: 1 · 2020: 1 · 2019: 7 · 2018: 3 · 2016: 8 · 2015: 3 · 2014: 8 · 2013: 11 · 2012: 5
- FWCI
- 3.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- gait analysis, markerless tracking, snakes, displacement field, 3D geometric models, body part tracking, image sequences
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- Meyer, D., Denzler, J., & Niemann, H. (2002). Model based extraction of articulated objects in image sequences for gait analysis. Proceedings of International Conference on Image Processing. https://doi.org/10.1109/icip.1997.631988
