The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Real-time tracking of non-rigid objects using mean shift
Abstract
A new method for real time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and finds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) and the target candidates is expressed by a metric derived from the Bhattacharyya coefficient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and efficient solution. The capability of the tracker to handle in real time partial occlusions, significant clutter, and target scale variations, is demonstrated for several image sequences.
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Citations
Cites
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
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
Self-citationIncremental Learning for Robust Visual Tracking
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061
Robust object tracking with online multiple instance learning
Babenko, B., Yang, M.-H., & Belongie, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.226 · Cited by 2,007
Visual tracking with online Multiple Instance Learning
Babenko, B., Yang, M.-H., & Belongie, S. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206737 · Cited by 1,770
Fast Online Object Tracking and Segmentation: A Unifying Approach
Wang, Q., Zhang, L., Bertinetto, L., Hu, W., & Torr, P. H. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00142 · Cited by 1,554
Real-Time Tracking via On-line Boosting
Grabner, H., Grabner, M., & Bischof, H. (2006) · Procedings of the British Machine Vision Conference 2006 · doi:10.5244/c.20.6 · Cited by 1,198
Siamese Instance Search for Tracking
Tao, R., Gavves, E., & Smeulders, A. W. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.158 · Cited by 1,208
Online selection of discriminative tracking features
Collins, R. T., Liu, Y., & Leordeanu, M. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.205 · Cited by 1,208
A Boosted Particle Filter: Multitarget Detection and Tracking
Okuma, K., Taleghani, A., de Freitas, N., Little, J. J., & Lowe, D. G. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24670-1_3 · Cited by 1,019
Integral histogram: a fast way to extract histograms in Cartesian spaces
Porikli, F. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.188 · Cited by 701
Visual tracking and recognition using appearance-adaptive models in particle filters
Zhou, S. K., Chellappa, R., & Moghaddam, B. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.836152 · Cited by 668
Local Trinary Patterns for human action recognition
Yeffet, L., & Wolf, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459201 · Cited by 339
New Generation Deep Learning for Video Object Detection: A Survey
Jiao, L., Zhang, R., Liu, F., Yang, S., Hou, B., Li, L., & Tang, X. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3053249 · Cited by 259
Automated Rock Detection and Shape Analysis from Mars Rover Imagery and 3D Point Cloud Data
Di, K., Yue, Z., Liu, Z., & Wang, S. (2013) · Journal of Earth Science · doi:10.1007/s12583-013-0316-3 · Cited by 42
Bottom dressing by a life-sized humanoid robot provided failure detection and recovery functions
Yamazaki, K., Oya, R., Nagahama, K., Okada, K., & Inaba, M. (2014) · 2014 IEEE/SICE International Symposium on System Integration · doi:10.1109/sii.2014.7028101 · Cited by 42
Unsupervised detection and tracking of moving objects for video surveillance applications
Elafi, I., Jedra, M., & Zahid, N. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.08.008 · Cited by 37
Object tracking based on improved MeanShift and SIFT
Du, K., Ju, Y., Jin, Y., Li, G., Li, Y., & Qian, S. (2012) · 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet) · doi:10.1109/cecnet.2012.6201691 · Cited by 27
A method of state recognition of dressing clothes based on dynamic state matching
Yamazaki, K., Oya, R., Nagahama, K., & Inaba, M. (2013) · Proceedings of the 2013 IEEE/SICE International Symposium on System Integration · doi:10.1109/sii.2013.6776728 · Cited by 20
Metadata
- Authors
- Comaniciu, D., Ramesh, V., & Meer, P.
- Venue
- Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Volume
- 2
- Pages
- 142-149
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2159128898
- Cited by
- 2,906
- References
- 1
- FWCI
- 67.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
