Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Actions Sketch: A Novel Action Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
TemporalBoost for Event Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Successive Convex Matching for Action Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Signal Processing - Part II
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This paper introduces an efficient technique for simultaneous processing of video frames to extract spatio-temporal features for fine activity detection and localization Such features, obtained through motion-selectivity attribute of 3D dual-tree complex wavelet transform (3D-DTCWT), are used to train a classifier for categorization of an incoming video The proposed learning model offers three core advantages: 1) significantly faster training stage than traditional supervised approaches, 2) volumetric processing of video data due to the use of 3D transform, 3) rich representation of human actions in view of directionality and shift-invariance of DTCWT No assumptions of scene background, location, objects of interest, or point of view information are made for activity learning whereas bidirectional 2D-PCA is employed to preserve structure and correlation amongst neighborhood pixels of a video frame Experimental results compare favorably to recently published results in literature.