International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Where Are the Ball and Players? Soccer Game Analysis with Color Based Tracking and Image Mosaick
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Particle Filter to Track Multiple Objects
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Parallel Tracking of All Soccer Players by Integrating Detected Positions in Multiple View Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Tracking Soccer Players using the Graph Representation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Interacting Multiple Model Particle Filter to Adaptive Visual Tracking
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Tracking and labelling of interacting multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A local-motion-based probabilistic model for visual tracking
Pattern Recognition
Sputnik Tracker: Having a Companion Improves Robustness of the Tracker
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Analysis of multi-agent activity using petri nets
Pattern Recognition
Multiple and variable target visual tracking for video-surveillance applications
Pattern Recognition Letters
A two-stage dynamic model for visual tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tracking people in broadcast sports
Proceedings of the 32nd DAGM conference on Pattern recognition
Visual tracking of multiple targets by multi-bernoulli filtering of background subtracted image data
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Visual tracking of numerous targets via multi-Bernoulli filtering of image data
Pattern Recognition
Video visualization for snooker skill training
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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In this paper we present an efficient algorithm for tracking multiple players during indoor sports matches. A sports match can be considered as a semi-controlled environment for which a set of closed-world assumptions regarding the visual as well as the dynamical properties of the players and the court can be derived. These assumptions are then used in the context of particle filtering to arrive at a computationally fast, closed-world, multi-player tracker. The proposed tracker is based on multiple, single-player trackers, which are combined using a closed-world assumption about the interactions among players. With regard to the visual properties, the robustness of the tracker is achieved by deriving a novel sports-domain-specific likelihood function and employing a novel background-elimination scheme. The restrictions on the player's dynamics are enforced by employing a novel form of local smoothing. This smoothing renders the tracking more robust and reduces the computational complexity of the tracker. We evaluated the proposed closed-world, multi-player tracker on a challenging data set. In comparison with several similar trackers that did not utilize all of the closed-world assumptions, the proposed tracker produced better estimates of position and prediction as well as reducing the number of failures.