The DOI matches its Crossref record; 53 of the 55 references it cites were found in a registry or an index.
CONDENSATION—Conditional Density Propagation for Visual Tracking
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Although object tracking has been studied for several decades, and much progress has been made in recent years [28, 16, 47, 5, 40, 26, 19], it remains a very challenging problem.”
From Object Tracking Benchmark (2015) · cites this paper as [28]
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
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,330
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Linear Statistical Inference and its Applications
Rao, C. R. (1973) · Wiley series in probability and statistics · doi:10.1002/9780470316436 · Cited by 10,474
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N., Salmond, D. J., & Smith, A. F. M. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0015 · Cited by 7,711
Fundamentals of speech recognition
Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680
GELB, A. (1974) · CERN Document Server (European Organization for Nuclear Research) · Cited by 6,405
Adaptive filtering, prediction and control
Ioannou, P. (1985) · Automatica · doi:10.1016/0005-1098(85)90014-7 · Cited by 4,465
Anderson, B. D. O., Moore, J. B., & Eslami, M. (1982) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1982.4308806 · Cited by 3,665
Research works printed but not linked: 1
- Menet, S., Saint-Marc, P., and Medioni, G. 1990. B-snakes: Implementation and application to stereo. In Proceedings DARPA, pp. 720-726.
Cited by
Sorted by citations
Items 101–200 of 1,002
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
Sidenbladh, H., Black, M. J., & Sigal, L. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47969-4_52 · Cited by 318
Conditional particle filters for simultaneous mobile robot localization and people-tracking
Montemerlo, M., Thrun, S., & Whittaker, W. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2002.1013439 · Cited by 318
The Way They Move: Tracking Multiple Targets with Similar Appearance
Dicle, C., Camps, O., & Sznaier, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.286 · Cited by 316
Visual Tracking via Locality Sensitive Histograms
He, S., Yang, Q., Lau, R. W. H., Wang, J., & Yang, M. (2013) · 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.2013.314 · Cited by 311
Extended Target Tracking Using Gaussian Processes
Wahlström, N., & Özkan, E. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2424194 · Cited by 308
Real Time Hand Pose Estimation Using Depth Sensors
Keskin, C., Kıraç, F., Kara, Y. E., & Akarun, L. (2012) · Springer eBooks · doi:10.1007/978-1-4471-4640-7_7 · Cited by 306
Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms
Candamo, J., Shreve, M., Goldgof, D. B., Sapper, D., & Kasturi, R. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2009.2030963 · Cited by 304
Self-Paced Learning for Long-Term Tracking
Supančič, J. S., & Ramanan, D. (2013) · 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.2013.308 · Cited by 298
An online learned CRF model for multi-target tracking
Yang, B., & Nevatia, R. (2012) · 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.2012.6247907 · Cited by 295
Vision-based and marker-less surgical tool detection and tracking: a review of the literature
Bouget, D., Allan, M., Stoyanov, D. V., & Jannin, P. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.09.003 · Cited by 292
Li, Y., Ai, H., Yamashita, T., Lao, S., & Kawade, M. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.73 · Cited by 288
Robust Visual Tracking using 1 Minimization
Mei, X., & Ling, H. (2009) · Cited by 286
A Survey of Vision-Based Traffic Monitoring of Road Intersections
Datondji, S. R. E., Dupuis, Y., Subirats, P., & Vasseur, P. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2530146 · Cited by 286
A multiple hypothesis approach to figure tracking
Cham, T., & Rehg, J. M. (2003) · 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.1999.784636 · Cited by 284
Oron, S., Bar-Hillel, A., Levi, D., & Avidan, S. (2012) · 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.2012.6247895 · Cited by 282
Coupled Detection and Trajectory Estimation for Multi-Object Tracking
Leibe, B., Konrad Schindler, B., & Gool, L. V. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408936 · Cited by 281
A Probabilistic Exclusion Principle for Tracking Multiple Objects
MacCormick, J., & Blake, A. (2000) · International Journal of Computer Vision · doi:10.1023/a:1008122218374 · Cited by 276
Self-citationKölsch, M., & Turk, M. (2004) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2004.1301601 · Cited by 276
Bae, S., & Yoon, K. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2691769 · Cited by 274
Kinematic jump processes for monocular 3D human tracking
Sminchisescu, C., & Triggs, B. (2003) · 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.2003.1211339 · Cited by 270
Model-based 3D tracking of an articulated hand
Stenger, B., Mendonça, P. R. S., & Cipolla, R. (2005) · 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.2001.990976 · Cited by 266
Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking
Vacchetti, L., Lepetit, V., & Fua, P. (2005) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar.2004.24 · Cited by 265
Thrun, S. (2002) · Cited by 264
Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches
Li, T., Sun, S., Sattar, T., & Corchado, J. M. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.031 · Cited by 263
Co-Tracking Using Semi-Supervised Support Vector Machines
Tang, F., Brennan, S., Zhao, Q., & Hai, T. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408954 · Cited by 256
Robust Multiperson Tracking from a Mobile Platform
Ess, A., Leibe, B., Konrad Schindler, B., & Gool, L. V. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.109 · Cited by 255
Bayesian Reconstruction of 3D Human Motion from Single-Camera Video
Howe, N. R., Leventon, M. E., & Freeman, W. T. (1999) · Smith ScholarWorks (Smith College) · Cited by 255
Finding approximate POMDP solutions through belief compression
Roy, N., Gordon, G., & Thrun, S. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1496 · Cited by 254
Object Tracking with an Adaptive Color-Based Particle Filter
Nummiaro, K., Koller-Meier, E., & Gool, L. V. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45783-6_43 · Cited by 252
Schwunk, S., Armbruster, N., Straub, S., Kehl, J., & Vetter, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.058 · Cited by 250
Machine Analysis of Facial Expressions
Pantic, M., & Stewart, M. (2007) · Face Recognition · doi:10.5772/4847 · Cited by 248
Low-Rank Sparse Learning for Robust Visual Tracking
Zhang, T., Ghanem, B., Liu, S., & Ahuja, N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_34 · Cited by 247
Estimating Articulated Human Motion With Covariance Scaled Sampling
Sminchisescu, C., & Triggs, B. (2003) · The International Journal of Robotics Research · doi:10.1177/027836403128965132 · Cited by 245
Saliency-based discriminant tracking
Mahadevan, V., & Vasconcelos, N. M. (2009) · 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.2009.5206573 · Cited by 243
Twin Gaussian Processes for Structured Prediction
Bo, L., & Sminchisescu, C. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-008-0204-y · Cited by 243
Dey, A. K., Hamid, R., Beckmann, C., Li, I., & Hsu, D. (2004) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/985692.985697 · Cited by 242
Thrun, S. (1999) · neural information processing systems · Cited by 241
A unified learning framework for real time face detection and classification
Shakhnarovich, G., Viola, P. A., & Moghaddam, B. (2003) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2002.1004124 · Cited by 241
A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces
Suk, H., & Lee, S. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.69 · Cited by 240
Using the CONDENSATION algorithm for robust, vision-based mobile robot localization
Dellaert, F., Burgard, W., Fox, D., & Thrun, S. (2003) · 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.1999.784976 · Cited by 240
On-line selection of discriminative tracking features
Collins, & Liu (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238365 · Cited by 239
Real-time hand tracking using a mean shift embedded particle filter
Shan, C., Tan, T., & Wei, Y. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.12.012 · Cited by 238
Multi-target particle filtering for the probability hypothesis density
Sidenbladh, H. (2003) · International Conference on Information Fusion (FUSION) · doi:10.1109/icif.2003.177321 · Cited by 235
Optimization and Filtering for Human Motion Capture
Gall, J., Rosenhahn, B., Brox, T., & Seidel, H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0173-1 · Cited by 234
Yang, M., Wu, Y. N., & Hua, G. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.146 · Cited by 233
Visual tracking and recognition using probabilistic appearance manifolds
Lee, K.-C., Ho, J. C. F., Yang, M., & Kriegman, D. J. (2005) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.02.002 · Cited by 233
Nonstoichiometry and stability in water of undoped SrCeO3
Mather, G. C., & Egea, J. R. J. (2003) · DIGITAL.CSIC (Spanish National Research Council (CSIC)) · doi:10.13039/501100000780 · Cited by 1
Hoey, J., Poupart, P., Bertoldi, A. V., Craig, T., Boutilier, C., & Mihailidis, A. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.06.008 · Cited by 229
Occlusion Models for Natural Images: A Statistical Study of a Scale-Invariant Dead Leaves Model
Lee, A. B., Mumford, D., & Huang, J. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011109015675 · Cited by 229
Kwok, C., Fox, D., & Meilă, M. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.823144 · Cited by 226
Advances in View-Invariant Human Motion Analysis: A Review
Ji, X., & Liu, H. (2009) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2009.2027608 · Cited by 221
Multiple camera tracking of interacting and occluded human motion
Dockstader, S. L., & Tekalp, A. M. (2001) · Proceedings of the IEEE · doi:10.1109/5.959340 · Cited by 221
Discriminative Density Propagation for 3D Human Motion Estimation
Sminchisescu, C., Kanaujia, A., Li, Z., & Metaxas, D. (2005) · 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.2005.132 · Cited by 221
Wolf, J., Burgard, W., & Bürkhardt, H. (2005) · IEEE Transactions on Robotics · doi:10.1109/tro.2004.835453 · Cited by 221
Verma, V., Gordon, G. J., Simmons, R. G., & Thrun, S. (2004) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2004.1310942 · Cited by 219
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
Yu, Q., Dinh, T. B., & Medioni, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_50 · Cited by 219
Xing, J., Ai, H., & Lao, S. (2009) · 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.2009.5206745 · Cited by 219
High-Throughput Quantification of Root Growth Using a Novel Image-Analysis Tool
French, A. P., Úbeda-Tomás, S., Holman, T. J., Bennett, M. J., & Pridmore, T. (2009) · PLANT PHYSIOLOGY · doi:10.1104/pp.109.140558 · Cited by 218
Probabilistic Kernels for the Classification of Auto-Regressive Visual Processes
Chan, A. B., & Vasconcelos, N. M. (2005) · 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.2005.279 · Cited by 217
Efficient Bayesian Phase Estimation
Wiebe, N., & Granade, C. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.010503 · Cited by 215
Object fusion tracking based on visible and infrared images: A comprehensive review
Zhang, X., Ye, P., Leung, H., Gong, K., & Xiao, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.05.002 · Cited by 214
Probabilistic Models for Inference about Identity
Li, P., Fu, Y., Mohammed, U., Elder, J. H., & Prince, S. J. D. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.104 · Cited by 212
Real-Time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes
Yang, T., Li, S. Z., Pan, Q., & Li, J. (2005) · 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.2005.292 · Cited by 209
Active Contours for Tracking Distributions
Freedman, D., & Zhang, T. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.821445 · Cited by 206
Thermistor at a Distance: Unobtrusive Measurement of Breathing
Fei, J., & Pavlidis, I. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2032415 · Cited by 201
Adaptive sampling for sensor networks
Jain, A., & Chang, E. Y. (2004) · doi:10.1145/1052199.1052202 · Cited by 199
Mode-finding for mixtures of Gaussian distributions
Carreira-Perpiñán, M. Á. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.888716 · Cited by 194
Home-Assistant Robot for an Aging Society
Yamazaki, K., Ueda, R., Nozawa, S., KOJIMA, M., Okada, K., Matsumoto, K., Ishikawa, M., Shimoyama, I., & Inaba, M. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2200563 · Cited by 193
Human-assisted motion annotation
Liu, C., Freeman, W. T., Adelson, E. H., & Weiss, Y. (2008) · 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.2008.4587845 · Cited by 193
Modeling and Tracking the Driving Environment With a Particle-Based Occupancy Grid
Danescu, R. G., Oniga, F., & Nedevschi, S. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158097 · Cited by 192
Robust Visual Tracking for Multiple Targets
Cai, Y., de Freitas, N., & Little, J. J. (2006) · Lecture notes in computer science · doi:10.1007/11744085_9 · Cited by 192
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments
Ess, A., Schindler, K., Leibe, B., & Van Gool, L. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910365417 · Cited by 191
Li, Y., Wang, T., & Shum, H. (2002) · ACM Transactions on Graphics · doi:10.1145/566654.566604 · Cited by 191
Real time hand pose estimation using depth sensors
Keskin, C., Kirac, F., Kara, Y. E., & Akarun, L. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130391 · Cited by 190
Video Tracking Using Learned Hierarchical Features
Wang, L., Liu, T., Wang, G., Chan, K. L., & Yang, Q. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2403231 · Cited by 190
Kernel particle filter for visual tracking
Chang, C., & Ansari, R. (2005) · IEEE Signal Processing Letters · doi:10.1109/lsp.2004.842254 · Cited by 190
Homography based multiple camera detection and tracking of people in a dense crowd
Eshel, R., & Moses, Y. (2008) · 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.2008.4587539 · Cited by 190
Monte Carlo Localization with Mixture Proposal Distribution
Thrun, S., Fox, D., & Burgard, W. (2000) · Cited by 190
PAMPAS: real-valued graphical models for computer vision
Isard, M. (2003) · 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.2003.1211410 · Cited by 190
Self-citationAn Introduction to Distributed Smart Cameras
Rinner, B., & Wolf, W. H. (2008) · Proceedings of the IEEE · doi:10.1109/jproc.2008.928742 · Cited by 189
Shirazi, M. S., & Morris, B. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2568920 · Cited by 188
Human activity detection and recognition for video surveillance
Niu, W., Long, J., Han, D., & Wang, Y.-F. (2005) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2004.1394293 · Cited by 187
Approximate Bayesian multibody tracking
Lanz, O. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.177 · Cited by 184
Fast occluded object tracking by a robust appearance filter
Nguyen, H. T., & Smeulders, A. W. M. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.45 · Cited by 183
A Learning Approach Towards Detection and Tracking of Lane Markings
Gopalan, R., Hong, T. H., Shneier, M. O., & Chellappa, R. (2012) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2012.2184756 · Cited by 182
Smal, I., Draegestein, K., Galjart, N., Niessen, W. J., & Meijering, E. H. W. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.916964 · Cited by 182
Monocular 3D Head Tracking to Detect Falls of Elderly People
Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2006) · Conference proceedings · doi:10.1109/iembs.2006.260829 · Cited by 182
People tracking using hybrid Monte Carlo filtering
Choo, K., & Fleet, D. J. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937643 · Cited by 181
Oron, S., Bar-Hillel, A., Levi, D., & Avidan, S. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-014-0740-6 · Cited by 179
Nonlinear filtering for speaker tracking in noisy and reverberant environments
Vermaak, J., & Blake, A. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.940294 · Cited by 179
Self-citationIntegrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach
Sivaraman, S., & Trivedi, M. M. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2246835 · Cited by 178
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
Lee, K.-C., & Kriegman, D. J. (2005) · 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.2005.260 · Cited by 177
Real time hand tracking by combining particle filtering and mean shift
Shan, C., Wei, Y., Tan, T., & Ojardias, F. (2004) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2004.1301611 · Cited by 173
Learning Multi-Task Correlation Particle Filters for Visual Tracking
Zhang, T., Xu, C., & Yang, M. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2797062 · Cited by 173
Efficient Multitarget Visual Tracking Using Random Finite Sets
Maggio, E., Taj, M., & Cavallaro, A. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.928221 · Cited by 172
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
Yogesh Rathi, A., Vaswani, N., Tannenbaum, A., & Yezzi, A. J. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1081 · Cited by 172
Sequential particle swarm optimization for visual tracking
Zhang, X., Hu, W., Maybank, S., Li, X., & Zhu, M. (2008) · 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.2008.4587512 · Cited by 172
A Rao-Blackwellized particle filter for eigentracking
Khan, Z., Balch, T. R., & Dellaert, F. (2004) · 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.2004.1315271 · Cited by 170
Adaptive Object Tracking Based on an Effective Appearance Filter
Wang, H., Suter, D., Konrad Schindler, B., & Shen, C. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1112 · Cited by 169
Face-TLD: Tracking-Learning-Detection applied to faces
Kalal, Z., Mikolajczyk, K., & Matas, J. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5653525 · Cited by 168
Metadata
- Authors
- Isard, M., & Blake, A.
- Affiliations
- University of Oxford (GB)
- ORCID
- Blake, Andrew
- Venue
- International Journal of Computer Vision
- Published
- 1998-08
- Volume
- 29
- Issue
- 1
- Pages
- 5-28
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2161406034
- Cited by
- 4,886
- References
- 55
- Citations by year
- 2026: 13 · 2025: 13 · 2024: 17 · 2023: 17 · 2022: 29 · 2021: 39 · 2020: 44 · 2019: 61 · 2018: 77 · 2017: 133 · 2016: 152 · 2015: 200 · 2014: 268 · 2013: 298 · 2012: 319
- FWCI
- 53.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- visual object tracking, curve tracking, condensation algorithm, non-Gaussian distributions, multiple hypothesis tracking
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
