The nature of statistical learning theory
The nature of statistical learning theory
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Tennis Video for High-level Content-based Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
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
Parameterized Modeling and Recognition of Activities
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A new method to segment playfield and its applications in match analysis in sports video
Proceedings of the 12th annual ACM international conference on Multimedia
Fast and accurate global motion compensation
Pattern Recognition
Automatic detection and recognition of athlete actions in diving video
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
View invariant action recognition using weighted fundamental ratios
Computer Vision and Image Understanding
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Motion analysis in broadcast sports video is a challenging problem especially for player action recognition due to the low resolution of players in the frames. In this paper, we present a novel approach to recognize the basic player actions in broadcast tennis video where the player is about 30 pixels tall. Two research challenges, motion representation and action recognition, are addressed. A new motion descriptor, which is a group of histograms based on optical flow, is proposed for motion representation. The optical flow here is treated as spatial pattern of noisy measurement instead of precise pixel displacement. To recognize the action performed by the player, support vector machine is employed to train the classifier where the concatenation of histograms is formed as the input features. Experimental results demonstrate that our method is promising by integrating with the framework of multimodal analysis in sports video.