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
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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
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Informatik, V. F. (2010)
Automated Blood Cells Extraction and counting
Zahran, B., Ayyoub, B. A., Zunait, R. S. A., & Sharadqeh, A. A. M. (2014) · INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY · doi:10.24297/ijct.v12i9.2831
Automated Kinematic Analysis Using Holistic Based Human Gait Motion for Biomedical Applications
Prakash, C., Mishra, U., Jain, M., Kumar, R., & Mittal, N. (2018) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence.2018.8442947
A Survey of Human Activity Interpretation in Image and Video Sequence
Xu, X., Chen, L., Zhang, X., Chen, D., Liu, X., & Fu, X. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3958-4.ch003
A fast and accurate moving target detection method
Wang, N. (2019) · Fifth Symposium on Novel Optoelectronic Detection Technology and Application · doi:10.1117/12.2521230
A Clustering Compression Method for 3D Human Motion Capture Data
Zhou, K., & Tian, F. (2014)
Chen, C.-H., Huang, D.-Y., Su, Y.-J., Lin, C.-E., Chen, T.-Y., Jiao, Z.-C., Wang, D.-J., & Wen, C.-K. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800142455005x
Yang, X., Huang, Y., Qi, Y., & Feng, B. (2024) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda61447.2024.10569884
Articulated human pose estimation in images and video
Zhu, A. (2016) · theses.fr (ABES) · doi:10.70675/f0d00da7z3113z4197z8effz4e382576a19d
Robust moving object detection by information fusion from multiple cameras
Ren, J. (2014) · University of Liverpool · doi:10.17638/00019013
Vision based target tracking for autonomous vehicles
Jia, Z. H. (2006) · doi:10.32657/10356/3466
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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