A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Automatic detection of 'Goal' segments in basketball videos
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
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
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Automatic Parsing of TV Soccer Programs
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Physics-Based 3D Position Analysis of a Soccer Ball from Monocular Image Sequences
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
An Effective and Fast Soccer Ball Detection and Tracking Method
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Monocular 3-D Tracking of the Golf Swing
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multi-Target Tracking - Linking Identities using Bayesian Network Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Tracking and labelling of interacting multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A scheme for ball detection and tracking in broadcast soccer video
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Event based indexing of broadcasted sports video by intermodalcollaboration
IEEE Transactions on Multimedia
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
Automatic Golf Ball Trajectory Reconstruction and Visualization
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Note: Low-resolution color-based visual tracking with state-space model identification
Computer Vision and Image Understanding
Multiple and variable target visual tracking for video-surveillance applications
Pattern Recognition Letters
Tactic analysis based on real-world ball trajectory in soccer video
Pattern Recognition
A method for identification of moving objects by integrative use of a camera and accelerometers
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Investigation on tracking system for real time video surveillance applications
Proceedings of the CUBE International Information Technology Conference
Video visualization for snooker skill training
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Soccer ball detection by comparing different feature extraction methodologies
Advances in Artificial Intelligence
Accurate ball detection in soccer images using probabilistic analysis of salient regions
Machine Vision and Applications
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This paper demonstrates innovative techniques for estimating the trajectory of a soccer ball from multiple fixed cameras. Since the ball is nearly always moving and frequently occluded, its size and shape appearance varies over time and between cameras. Knowledge about the soccer domain is utilized and expressed in terms of field, object and motion models to distinguish the ball from other movements in the tracking and matching processes. Using ground plane velocity, longevity, normalized size and color features, each of the tracks obtained from a Kalman filter is assigned with a likelihood measure that represents the ball. This measure is further refined by reasoning through occlusions and back-tracking in the track history. This can be demonstrated to improve the accuracy and continuity of the results. Finally, a simple 3D trajectory model is presented, and the estimated 3D ball positions are fed back to constrain the 2D processing for more efficient and robust detection and tracking. Experimental results with quantitative evaluations from several long sequences are reported.