A multidimensional approach to detect action scene in video data
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Volumetric Features for Video Event Detection
International Journal of Computer Vision
Action recognition for surveillance applications using optic flow and SVM
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Histograms of optical flow for efficient representation of body motion
Pattern Recognition Letters
Learning group activity in soccer videos from local motion
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Detection of tennis court lines for sport video categorization
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
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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. This paper presents 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. The experimental results demonstrate that our method is promising.