Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
View-Invariant Human Activity Recognition Based on Shape and Motion Features
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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We address the problem of action recognition. Our aim is to recognize single person activities in surveillance scenes. To meet the requirements of real scene action recognition, we present a compact motion representation for human activity recognition. With the employment of efficient features extracted from optical flow as the main part, together with global information, our motion representation is compact and discriminative. We also build a novel human action dataset(CASIA) in surveillance scene with three vertically different viewpoints and distant people. Experiments on CASIA dataset and WEIZMANN dataset show that our method can achieve satisfying recognition performance with low computational cost as well as robustness against both horizontal( panning) and vertical(tilting) viewpoint changes.