The nature of statistical learning theory
The nature of statistical learning theory
Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Rule-based video classification system for basketball video indexing
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
A unified framework for semantic shot classification in sports videos
Proceedings of the tenth ACM international conference on Multimedia
Real-Time Tracking for Enhanced Tennis Broadcasts
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Video Annotation for Content-based Retrieval using Human Behavior Analysis and Domain Knowledge
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Classification of Team Behaviors in Sports Video Games
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Adaptive extraction of highlights from a sport video based on excitement modeling
IEEE Transactions on Multimedia
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Combining inertial and visual sensing for human action recognition in tennis
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
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Stroke recognition in tennis is important for building up statistics of the player and also quickly analyzing the player. It is difficult primarily on account of low resolution, variability in strokes of the same player as well as among players, variations in background, weather and illumination conditions. This paper proposes a technique to automatically classify tennis strokes efficiently under these varying circumstances. We use the geometrical information of the player to classify the strokes. The player is modeled using a color histogram and tracked across the video using histogram back projection. The binarized (segmented) output of the tracker is skeletonized and the gradient information of the skeleton is extracted to form a feature vector. A three class SVM classifier is then used to classify the stroke to be a Forehand, Backhand or Neither. We evaluated the performance of our approach with real world datasets and have obtained promising results. Finally, the proposed approach is real time and can be used with live tennis broadcasts.