Spatio-temporal steerable pyramid for human action recognition

Xiantong Zhen, Ling Shao

Research output: Contribution to conferencePaper

11 Citations (Scopus)


In this paper, we propose a novel holistic representation based on the spatio-temporal steerable pyramid (STSP) for human action recognition. The spatio-temporal Laplacian pyramid provides an effective technique for multi-scale analysis of video sequences. By decomposing spatio-temporal volumes into band-passed sub-volumes, spatio-temporal patterns residing in different scales will be nicely localized. Then three-dimensional separable steerable filters are conducted on each of the sub-volume to capture the spatio-temporal orientation information efficiently. The outputs of the quadrature pair of steerable filters are squared and summed to yield a more robust measure of motion energy. To make the representation invariant to shifting and applicable with coarsely-extracted bounding boxes for the performed actions, max pooling operations are employed between responses of the filtering at adjacent scales, and over spatio-temporal local neighborhoods. Taking advantage of multi-scale and multi-orientation analysis and feature pooling, STSP produces a compact but informative and invariant representation of human actions. We conduct extensive experiments on the KTH, IXMAS and HMDB51 datasets, and the proposed STSP achieves comparable results with the state-of-the-art methods.
Original languageEnglish
Publication statusPublished - Apr 2013
Event10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition - Shanghai, China
Duration: 1 Apr 2013 → …


Conference10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition
Period1/04/13 → …


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