Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
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
International Journal of Computer Vision
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
A differential geometric approach to representing the human actions
Computer Vision and Image Understanding
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Recognizing human actions is currently one of the most active research topics. Efros et al. first proposed using optical flow and normalized correlation to recognize distant actions. One weakness of the method is that optical flow is too noisy and cannot reveal the true motions; the other popular method is the space-time-interest-points proposed by Laptev et al., who extended the Harris corner detector to temporal domain. Inspired by the two methods, we proposed a new algorithm based on displacement of Lowe's scale-invariant key points to detect motions. The vectors of matched key points are calculated as weighted orientation histograms and then classified by SVM. Experimental results demonstrate that the proposed motion descriptor is effective on recognizing both general and sport actions.