A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Action Recognition in Broadcast Tennis Video
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Team activity recognition in sports
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
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This paper proposes a local motion-based approach for recognizing group activities in soccer videos. Given the SIFT keypoint matches on two successive frames, we propose a simple but effective method to group these keypoints into the background point set and the foreground point set. The former one is used to estimate camera motion and the latter one is applied to represent group actions. After camera motion compensation, we apply a local motion descriptor to characterize relative motion between corresponding keypoints on two consecutive frames. The novel descriptor is effective in representing group activities since it focuses on local motion of individuals and excludes noise such as background motion caused by inaccurate compensation. Experimental results show that our approach achieves high recognition rates in soccer videos and is robust to inaccurate compensation results.