Abstract
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.
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Minhas, R., Baradarani, A., Seifzadeh, S., Wu, Q.M.J. (2010). Human Action Recognition Using Non-separable Oriented 3D Dual-Tree Complex Wavelets. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_22
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DOI: https://doi.org/10.1007/978-3-642-12297-2_22
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