The DOI matches its Crossref record; 87 of the 88 references it cites were found in a registry or an index.
Kernel-based object tracking
Abstract
A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face 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 [16]
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
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 12,040
Savitzky-Golay Smoothing Filters
Press, W. H., & Teukolsky, S. A. (1990) · Computers in Physics · doi:10.1063/1.4822961 · Cited by 11,851
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531
Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,523
Self-citationIntroduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,058
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
Divergence measures based on the Shannon entropy
Lin, J.-H. (1991) · IEEE Transactions on Information Theory · doi:10.1109/18.61115 · Cited by 5,877
Cited by
Sorted by citations
Items 501–600 of 1,001
Visual tracking by partition-based histogram backprojection and maximum support criteria
Lee, J.-Y., & Yu, W. (2011) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2011.6181739 · Cited by 43
Semi-supervised learning for character recognition in historical archive documents
Richarz, J., Vajda, S., Grzeszick, R., & Fink, G. A. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.07.013 · Cited by 44
The State-of-the-Art in Visual Object Tracking
Jalal, A. S., & Singh, V. (2012) · Informatica · Cited by 43
Visual Object Tracking Based on Mean-shift and Particle-Kalman Filter
Iswanto, I. A., & Li, B. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.10.010 · Cited by 43
Eom, T.-Y., Han, S. B., Kim, J., Blundon, J. A., Wang, Y., Yu, J., Anderson, K., Kaminski, D. B., Sakurada, S. M., Pruett‐Miller, S. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14628-y · Cited by 47
Micromanipulation Using Reconfigurable Self-Assembled Magnetic Droplets With Needle Guidance
Wang, Q., Yang, L., & Zhang, L. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3062779 · Cited by 43
Action Recognition in Broadcast Tennis Video Using Optical Flow and Support Vector Machine
Zhu, G., Xu, C., Gao, W., & Huang, Q. (2006) · Lecture notes in computer science · doi:10.1007/11754336_9 · Cited by 43
Shape of Gaussians as feature descriptors
Gong, L., Wang, T., & Liu, F. (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.2009.5206506 · Cited by 43
Action Recognition in Broadcast Tennis Video
Zhu, G., Xu, C., Huang, Q., & Gao, W. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.205 · Cited by 43
Tracking Randomly Moving Objects on Edge Box ProposalsPreprint
Zhu, G., Porikli, F., & Li, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.08085 · Cited by 43
Context and profile based cascade classifier for efficient people detection and safety care system
Lee, K., Nam, M. Y., Chung, K., Lee, Y., & Kang, U.-G. (2012) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1020-5 · Cited by 42
A multiple template approach for robust tracking of fast motion target
Sun, J., He, F., Chen, Y., & Chen, X. (2016) · Applied mathematics/Applied Mathematics. A Journal of Chinese Universities/Gao-xiao yingyong shuxue xuebao · doi:10.1007/s11766-016-3378-z · Cited by 42
Wu, H., Liu, N., Luo, X., Su, J., & Chen, L. (2013) · Signal Image and Video Processing · doi:10.1007/s11760-013-0576-5 · Cited by 42
PageRank Tracker: From Ranking to Tracking
Gong, C., Fu, K., Łoza, A., Wu, Q., Liu, J., & Yang, J. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2274516 · Cited by 42
Design and Control of an Embedded Vision Guided Robotic Fish with Multiple Control Surfaces
Yu, J., Wang, K., Tan, M. H., & Zhang, J. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/631296 · Cited by 42
Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm
Cho, H., Rybski, P. E., & Zhang, W. (2010) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2010.5624993 · Cited by 42
Soybean varieties portfolio optimisation based on yield prediction
Marko, O., Brdar, S., Panić, M. N., Lugonja, P., & Crnojevic, V. S. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2016.07.009 · Cited by 42
Robust visual object tracking using context-based spatial variation via multi-feature fusion
Elayaperumal, D., & Joo, Y. H. (2021) · Information Sciences · doi:10.1016/j.ins.2021.06.084 · Cited by 42
MAKING OBJECT LEARNING AND RECOGNITION AN ACTIVE PROCESS
Ude, A., Omrčen, D., & Cheng, G. (2008) · International Journal of Humanoid Robotics · doi:10.1142/s0219843608001406 · Cited by 42
Derpanis, K. G. (2008) · Cited by 41
Visual Saliency Based Object Tracking
Zhang, G., Yuan, Z., Zheng, N., Sheng, X., & Liu, T. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12304-7_19 · Cited by 41
Effective Appearance Model and Similarity Measure for Particle Filtering and Visual Tracking
Wang, H., Suter, D., & Konrad Schindler, B. (2006) · Lecture notes in computer science · doi:10.1007/11744078_47 · Cited by 41
Probabilistic tracking on Riemannian manifolds
Wu, Y., Wu, B., Liu, J., & Lu, H. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761046 · Cited by 41
Webcam-Based Visual Gaze Estimation
Valenti, R., Staiano, J., Sebe, N., & Gevers, T. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04146-4_71 · Cited by 41
Deep transfer across domains for face antispoofing
Tu, X., Zhang, H., Xie, M., Luo, Y., Zhang, Y., & Ma, Z. (2019) · Journal of Electronic Imaging · doi:10.1117/1.jei.28.4.043001 · Cited by 41
Ship target detection and tracking in cluttered infrared imagery
Wu, J., Mao, S., Wang, X., & Zhang, T. (2011) · Optical Engineering · doi:10.1117/1.3578402 · Cited by 41
Contact-Free Heart Rate Measurement Using a Camera
Lee, K., Hung, P.-C., & Tsai, L.-W. (2012) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2012.27 · Cited by 41
Sensor Fusion for Video Surveillance
Snidaro, L., Foresti, G. L., Niu, R., & Varshney, P. K. (2004) · Syracuse University Libraries (Syracuse University) · Cited by 41
A compact association of particle filtering and kernel based object tracking
Yao, A., Lin, X., Wang, G., & Yu, S. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.01.016 · Cited by 40
Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering
Karavasilis, V., Nikou, C., & Likas, A. C. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2010.12.002 · Cited by 40
Phase Based Modelling of Dynamic Textures
Ghanem, B., & Ahuja, N. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409094 · Cited by 40
Mean-Shift and Sparse Sampling-Based SMC-PHD Filtering for Audio Informed Visual Speaker Tracking
Kılıç, V., Barnard, M., Wang, W., Hilton, A., & Kittler, J. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2599150 · Cited by 40
A robust algorithm for detecting people in overhead views
Ahmed, I., & Adnan, A. (2017) · Cluster Computing · doi:10.1007/s10586-017-0968-3 · Cited by 40
Dynamic objects elimination in SLAM based on image fusion
Fan, Y., Han, H. W., Tang, Y., & Zhi, T. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.10.024 · Cited by 40
Ahmed, J., Jafri, M. N., Shah, M. A., & Akbar, M. A. (2007) · Machine Vision and Applications · doi:10.1007/s00138-007-0072-4 · Cited by 40
The World of Fast Moving Objects
Rozumnyi, D., Kotera, J., Šroubek, F., Novotny, L., & Matas, J. (2017) · 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.2017.514 · Cited by 40
Computer Vision Applications In Construction And Asset Management Phases: A Literature Review
Jiang, Z., & Messner, J. (2023) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2023.009 · Cited by 39
Contrastive Mean-Shift Learning for Generalized Category Discovery
Choi, S., Kang, D., & Cho, M. (2024) · 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/cvpr52733.2024.02179 · Cited by 39
Regional heart motion abnormality detection: An information theoretic approach
Punithakumar, K., Ayed, I. B., Islam, A., Goela, A., Ross, I. G., Chong, J., & Li, S. (2013) · Medical Image Analysis · doi:10.1016/j.media.2012.11.007 · Cited by 41
Ayed, I. B., Chen, H.-M., Punithakumar, K., Ross, I., & Li, S. (2010) · 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.2010.5540045 · Cited by 39
Kernel-based object tracking using asymmetric kernels with adaptive scale and orientation selection
Yılmaz, A. (2009) · Machine Vision and Applications · doi:10.1007/s00138-009-0237-4 · Cited by 39
Unsupervised video anomaly detection using feature clustering
Li, H., Achim, A., & Bull, D. (2012) · IET Signal Processing · doi:10.1049/iet-spr.2011.0074 · Cited by 39
Salehi, M., Ahmadi, S., Prevost, R., Navab, N., & Wein, W. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3_61 · Cited by 39
Robust object tracking based on adaptive templates matching via the fusion of multiple features
Li, Z., Gao, S., & Nai, K. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.01.012 · Cited by 39
On-board monitoring system for road traffic safety analysis
Battiato, S., Farinella, G. M., Gallo, G., & Giudice, O. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.02.014 · Cited by 39
Gaussian-response correlation filter for robust visual object tracking
Moorthy, S., Choi, J. Y., & Joo, Y. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.016 · Cited by 39
Nalli, G., Amendola, D., Perali, A., & Mostarda, L. (2021) · Applied Sciences · doi:10.3390/app11135800 · Cited by 39
Object tracking by adaptive feature extraction
Han, B., & Davis, L. S. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2004.1421349 · Cited by 40
AdaPT: Real-time adaptive pedestrian tracking for crowded scenes
Bera, A., Galoppo, N., Sharlet, D., Lake, A., & Manocha, D. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907095 · Cited by 39
Fast global kernel density mode seeking with application to localization and tracking
Shen, C., Brooks, M. J., & van den Hengel, A. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.94 · Cited by 39
Convergence of a Mean Shift Algorithm
Li, X. (2005) · Journal of Software · doi:10.1360/jos160365 · Cited by 39
A Markov random field approach for microstructure synthesis
Kumar, A., Nguyen, L., DeGraef, M., & Sundararaghavan, V. (2016) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/0965-0393/24/3/035015 · Cited by 39
Robust Visual Tracking Using an Effective Appearance Model Based on Sparse Coding
Zhang, S., Yao, H., Sun, X., & Liu, S. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2168752.2168757 · Cited by 38
Remote Photoplethysmography for Heart Rate and Blood Oxygenation Measurement: A Review
Pirzada, P., Wilde, A., & Harris‐Birtill, D. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3405414 · Cited by 38
Visual Tracking Using a Pixelwise Spatiotemporal Oriented Energy Representation
Cannons, K. J., Gryn, J. M., & Wildes, R. P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_37 · Cited by 38
Robust Object Tracking Via Active Feature Selection
Zhang, K., Lei Zhang, A., Yang, M., & Hu, Q. (2013) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2013.2269772 · Cited by 38
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
Ommer, B., Mader, T., & Buhmann, J. M. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0211-7 · Cited by 38
A Framework for the Analysis of Process Mining Algorithms
Weber, P., Bordbar, B., & Peter Tiňo, B. (2013) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmca.2012.2195169 · Cited by 38
Collaborative Control of the Manus Manipulator
Driessen, B., Kate, T. K. T., Liefhebber, F., Versluis, A. H. G., & van Woerden, J. A. (2005) · Universal Access in the Information Society · doi:10.1007/s10209-005-0121-6 · Cited by 38
Consistent methods for Decentralised Data Fusion using Particle Filters
Ong, L. S., Upcroft, B., Ridley, M., Bailey, T., Sukkarieh, S., & Durrant-Whyte, H. F. (2006) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi.2006.265604 · Cited by 38
Pair-activity classification by bi-trajectories analysis
Zhou, Y., Yan, S., & Huang, T. S. (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.2008.4587816 · Cited by 38
Zhou, X., Li, Y., & He, B. (2013) · Signal Processing · doi:10.1016/j.sigpro.2013.08.002 · Cited by 38
Fusion with Diffusion for Robust Visual Tracking
Zhou, Y. C., Bai, X., Liu, W., & Latecki, L. J. (2012) · Cited by 38
Multiple Feature Fusion via Weighted Entropy for Visual Tracking
Ma, L., Lu, J., Feng, J., & Zhou, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.358 · Cited by 38
An Automated Method for Cell Detection in Zebrafish
Liu, T., Li, G., Nie, J., Tarokh, A. B., Zhou, X., Guo, L., Malicki, J. J., Xia, W., & Wong, S. T. C. (2008) · Neuroinformatics · doi:10.1007/s12021-007-9005-7 · Cited by 38
Memristive Threshold Logic Circuit Design of Fast Moving Object Detection
Maan, A. K., Kumar, D. S., Sugathan, S., & James, A. P. (2014) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2014.2359801 · Cited by 37
Adaptive NormalHedge for robust visual tracking
Zhang, S., Zhou, H., Yao, H., Zhang, Y., Wang, K., & Zhang, J. (2014) · Signal Processing · doi:10.1016/j.sigpro.2014.08.027 · Cited by 37
Yuan, L., Zheng, Y. F., Zhu, J., Wang, L., & Brown, A. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2165554 · Cited by 37
Understanding dynamic scenes based on human sequence evaluation
Gonzàlez, J., Rowe, D. B., Varona, J., & Roca, F. X. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.02.004 · Cited by 37
Visual Tracking Using Pertinent Patch Selection and Masking
Lee, D.-Y., Sim, J.-Y., & Kim, C. (2014) · 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.2014.446 · Cited by 37
Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking
Yin, S., Na, J. H., Choi, J. Y., & Oh, S. (2011) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2011.02.010 · Cited by 38
Adaptive Mean-Shift Tracking With Auxiliary Particles
Wang, J., & Yagi, Y. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2021482 · Cited by 38
Automated lecture recording system
Chou, H.-P., Wang, J.-M., Fuh, C., Lin, S., & Chen, S.-W. (2010) · International Conference on System Science and Engineering · doi:10.1109/icsse.2010.5551811 · Cited by 37
Automatic detection of salient objects and spatial relations in videos for a video database system
Sevilmiş, T., Baştan, M., Güdükbay, U., & Ulusoy, Ö. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.01.001 · Cited by 37
Robust visual tracking using joint scale-spatial correlation filters
Zhang, M., Xing, J., Gao, J. G., & Hu, W. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351044 · Cited by 37
Tracking of Group Space Objects within Bayesian Framework
黄, 剑., & Hu, W. (2013) · JOURNAL OF RADARS · doi:10.3724/sp.j.1300.2012.20079 · Cited by 37
Jeong, J., Yoon, T. S., & Park, J. B. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.043 · Cited by 37
Multiple Pedestrian Tracking From Monocular Videos in an Interacting Multiple Model Framework
Jiang, Z., & Huynh, D. Q. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2779856 · Cited by 37
Li, H., Chen, L., Li, F., & Huang, M. (2019) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.13.026511 · Cited by 40
Online Multi-Expert Learning for Visual Tracking
Li, Z., Wei, W., Zhang, T., Wang, M., Hou, S., & Xin, P. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2931082 · Cited by 37
Moving Object Detection Based on Background Compensation and Deep Learning
Zhu, J., Wang, Z., Wang, S., & Chen, S. (2020) · Symmetry · doi:10.3390/sym12121965 · Cited by 37
Dicoogle, a Pacs Featuring Profiled Content Based Image Retrieval
Valente, F., Costa, C. M. A., & Silva, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061888 · Cited by 37
Track and Cut: Simultaneous Tracking and Segmentation of Multiple Objects with Graph Cuts
Bugeau, A., & Pérez, P. (2008) · EURASIP Journal on Image and Video Processing · doi:10.1155/2008/317278 · Cited by 39
An Adaptive Binning Color Model for Mean Shift Tracking
Li, P. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.928528 · Cited by 37
Cooperative Object Tracking with Multiple PTZ Cameras
Everts, I., Sebe, N., & Jones, G. A. (2007) · International Conference on Image Analysis and Processing · doi:10.1109/iciap.2007.4362799 · Cited by 37
Robust target detection and tracking through integration of motion, color, and geometry
Veeraraghavan, H., Schrater, P., & Papanikolopoulos, N. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.04.003 · Cited by 37
Adaptive Probabilistic Tracking Embedded in a Smart Camera
Fleck, S., & Straßer, W. (2006) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvpr.2005.404 · Cited by 37
On Probabilistic Applicative Bisimulation and Call-by-Value λ-Calculi
Crubillé, R., & Lago, U. D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-642-54833-8_12 · Cited by 36
Robust Object Tracking Based on Principal Component Analysis and Local Sparse Representation
Liu, H., Shutao Li, A., & Fang, L. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2015.2437636 · Cited by 36
ARCADE: Augmented Reality Computing Arena for Digital Entertainment
Lyu, M. R., King, I., Wong, T.-T., Yau, E., & Chan, P. (2005) · IEEE Aerospace Conference · doi:10.1109/aero.2005.1559626 · Cited by 39
Spatio-Temporal Analysis for Human Action Detection and Recognition in Uncontrolled Environments
Liu, D., Yan, Y., Shyu, M., Zhao, G., & Chen, M. (2015) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.2015010101 · Cited by 36
Adaptive and discriminative metric differential tracking
Jiang, N., Liu, W., & Wu, Y. N. (2011) · 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.2011.5995716 · Cited by 36
Logo information recognition in large-scale social media data
Wang, F., Qi, S., Gao, G., Zhao, S., & Wang, X. (2014) · Multimedia Systems · doi:10.1007/s00530-014-0393-x · Cited by 36
Online adaptive radial basis function networks for robust object tracking
Babu, R. V., Suresh, S., & Makur, A. (2009) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.10.004 · Cited by 36
Tracking objects using shape context matching
Liu, Z., Shen, H., Feng, G., & Hu, D. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.11.012 · Cited by 36
Vision-based bicyclist detection and tracking for intelligent vehicles
Cho, H., Rybski, P. E., & Zhang, W. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5548063 · Cited by 36
Lee, K. J., Bong, K., Kim, C., Jang, J., Lee, K.-R., Lee, J. H., Kim, G., & Yoo, H. (2016) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2016.2617317 · Cited by 36
Real-time dance evaluation by markerless human pose estimation
Kim, Y.-H., & Kim, D. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6068-4 · Cited by 36
Spatially Regularized Structural Support Vector Machine for Robust Visual Tracking
Zheng, Y., Sun, L., Wang, S., Zhang, J., & Ning, J. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2855686 · Cited by 36
Kalman Filter for Spatial-Temporal Regularized Correlation Filters
Feng, S., Hu, K., Fan, E., Zhao, L., & Wu, C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3060164 · Cited by 36
Metadata
- Authors
- Comaniciu, D., Ramesh, V., & Meer, P.
- Affiliations
- Siemens (United States) (US)
- Institute of Electrical and Electronics Engineers (US)
- Rutgers, The State University of New Jersey (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2003-05
- Volume
- 25
- Issue
- 5
- Pages
- 564-577
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2132103241
- Cited by
- 4,560
- References
- 88
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 47 · 2023: 46 · 2022: 51 · 2021: 96 · 2020: 108 · 2019: 150 · 2018: 181 · 2017: 224 · 2016: 270 · 2015: 336 · 2014: 397 · 2013: 363 · 2012: 379
- FWCI
- 134.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Infrared Target Detection Methodologies
- Keywords
- object tracking, kernel-based regularization, feature histograms, spatial masking, isotropic kernels, Bhattacharyya coefficient, mean shift, target localization, camera movement, partial occlusion, target scale variation, data association, face 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
