The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Online selection of discriminative tracking features
Abstract
This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. Given a set of seed features, we compute log likelihood ratios of class conditional sample densities from object and background to form a new set of candidate features tailored to the local object/background discrimination task. The two-class variance ratio is used to rank these new features according to how well they separate sample distributions of object and background pixels. This feature evaluation mechanism is embedded in a mean-shift tracking system that adaptively selects the top-ranked discriminative features for tracking. Examples are presented that demonstrate how this method adapts to changing appearances of both tracked object and scene background. We note susceptibility of the variance ratio feature selection method to distraction by spatially correlated background clutter and develop an additional approach that seeks to minimize the likelihood of distraction.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
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
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
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
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
Mean shift, mode seeking, and clustering
Cheng, Y. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.400568 · Cited by 3,856
Texture features for browsing and retrieval of image data
Manjunath, B. S., & Ma, W. Y. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.531803 · Cited by 3,727
Design and Analysis of Modern Tracking Systems
Blackman, S. S., & Populi, R. (1999) · Cited by 3,509
Learning patterns of activity using real-time tracking
Stauffer, C., & Grimson, W. E. L. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868677 · Cited by 3,248
Bar‐Shalom, Y. (1988) · Medical Entomology and Zoology · Cited by 3,157
Bar-Shalom, Y., Fortmann, T. E., & Cable, P. G. (1990) · The Journal of the Acoustical Society of America · doi:10.1121/1.398863 · Cited by 3,070
An algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
Real-time tracking of non-rigid objects using mean shift
Comaniciu, D., Ramesh, V., & Meer, P. (2002) · Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) · doi:10.1109/cvpr.2000.854761 · Cited by 2,906
Modeling TCP throughput: a simple model and its empirical validation
Padhye, J., Firoiu, V., Towsley, D., & Kurose, J. (1998) · Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication · doi:10.1145/285237.285291 · Cited by 2,106
Active Appearance Models Revisited
Matthews, I., & Baker, S. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029666.37597.d3 · Cited by 1,535
The macroscopic behavior of the TCP congestion avoidance algorithm
Mathis, M., Semke, J., Mahdavi, J., & Ott, T. (1997) · ACM SIGCOMM Computer Communication Review · doi:10.1145/263932.264023 · Cited by 1,495
Computer Vision Face Tracking For Use in a Perceptual User Interface
Gary, R. B. (1998) · Cited by 1,400
An integrated experimental environment for distributed systems and networks
White, B., Lepreau, J., Stoller, L. B., Ricci, R., Guruprasad, S., Newbold, M., Hibler, M., Barb, C., & Joglekar, A. A. (2002) · ACM SIGOPS Operating Systems Review · doi:10.1145/844128.844152 · Cited by 1,302
Receiver-driven layered multicast
McCanne, S., Jacobson, V., & Vetterli, M. (1996) · Conference proceedings on Applications, technologies, architectures, and protocols for computer communications · doi:10.1145/248156.248168 · Cited by 1,195
A digital fountain approach to reliable distribution of bulk data
Byers, J. W., Luby, M., Mitzenmacher, M., & Rege, A. (1998) · Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication · doi:10.1145/285237.285258 · Cited by 1,138
Architectural considerations for a new generation of protocols
Clark, D. D., & Tennenhouse, D. L. (1990) · Proceedings of the ACM symposium on Communications architectures & protocols · doi:10.1145/99508.99553 · Cited by 1,064
Matthews, L., Ishikawa, T., & Baker, S. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.16 · Cited by 754
Model-based object tracking in monocular image sequences of road traffic scenes
Roller, D., Daniilidis, K., & Nagel, H. H. (1993) · International Journal of Computer Vision · doi:10.1007/bf01539538 · Cited by 651
Real time face and object tracking as a component of a perceptual user interface
Bradski, G. R. (2002) · Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201) · doi:10.1109/acv.1998.732882 · Cited by 627
Algorithms for cooperative multisensor surveillance
Collins, R. T., Lipton, A. J., Fujiyoshi, H., & Kanade, T. (2001) · Proceedings of the IEEE · doi:10.1109/5.959341 · Cited by 599
Self-citationTCP-like congestion control for layered multicast data transfer
Vicisano, L., Crowcroft, J., & Rizzo, L. (2002) · doi:10.1109/infcom.1998.662909 · Cited by 522
Neural Networks for Pattern Recognition.
Lange, N., Bishop, C. M., & Ripley, B. D. (1997) · Journal of the American Statistical Association · doi:10.2307/2965437 · Cited by 465
Probabilistic data association methods for tracking complex visual objects
Rasmussen, C., & Hager, G. D. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927458 · Cited by 376
Comparison of five color models in skin pixel classification
Zarit, B. D., Super, B. J., & Quek, F. K. H. (2003) · Proceedings International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems. In Conjunction with ICCV'99 (Cat. No.PR00378) · doi:10.1109/ratfg.1999.799224 · Cited by 333
Spatial Color Indexing and Applications
Huang, J., Ravi Kumar, S., Mitra, M., Zhu, W.-J., & Zabih, R. (1999) · International Journal of Computer Vision · doi:10.1023/a:1008108327226 · Cited by 271
ASSET-2: real-time motion segmentation and shape tracking
Smith, S. M., & Brady, J. M. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.400573 · Cited by 269
Extending equation-based congestion control to multicast applications
Widmer, J., & Handley, M. J. (2001) · ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM) · doi:10.1145/383059.383081 · Cited by 246
An EM-like algorithm for color-histogram-based object tracking
Zivkovic, Z., & Krose, B. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315113 · Cited by 241
Probabilistic tracking in joint feature-spatial spaces
Elgammal, A., Duraiswami, R., & Davis, L. S. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211432 · Cited by 170
Facial asymmetry quantification for expression invariant human identification
Liu, Y., Schmidt, K. L., Cohn, J. F., & Mitra, S. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00078-x · Cited by 134
Self-citationLiu, Y., Teverovskiy, L., Carmichael, O. T., Kikinis, R., Shenton, M. E., Carter, C. S., Stenger, V. A., Davis, S. W., Aizenstein, H. J., Becker, J. T., et al. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30135-6_48 · Cited by 98
Self-citationMLDA: a TCP-friendly congestion control framework for heterogeneous multicast environments
Sisalem, D., & Wolisz, A. (2002) · doi:10.1109/iwqos.2000.847939 · Cited by 97
Joint probabilistic techniques for tracking multi-part objects
Rasmussen, C. E., & Hager, G. D. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1998.698582 · Cited by 88
Adaptive color space switching for face tracking in multi-colored lighting environments
Stern, H. I., & Efros, B. (2003) · doi:10.1109/afgr.2002.1004162 · Cited by 83
Facial asymmetry quantification for expression invariant human identification
Liu, Y., Schmidt, K. L., Cohn, J. F., & Weaver, R. (2003) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2002.1004156 · Cited by 45
Self-citationBootstrap Initialization of Nonparametric Texture Models for Tracking
Toyama, K., & Wu, Y. N. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45053-x_8 · Cited by 12
Cited by
Online Object Tracking: A Benchmark
Wu, Y., Lim, J., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.312 · Cited by 4,138
Wu, Y., Lim, J., & Yang, M.-H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2388226 · Cited by 3,371
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
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
Metadata
- Authors
- Collins, R. T., Liu, Y., & Leordeanu, M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2005-10
- Volume
- 27
- Issue
- 10
- Pages
- 1631-1643
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2150000644
- Cited by
- 1,208
- References
- 42
- FWCI
- 53.85
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
