ACTION RECOGNITION USING SPATIO-TEMPORAL FEATURE EXTRACTION
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The importance of automatically recognition of human activities and it's ap- plications increase as the technology is growing fast these days. In this thesis we focus on the action recognition techniques using 3D feature extraction techniques and try to improve the performance of the available feature extraction methods to have more accurate action classi cation. This thesis is based on spatio-temporal feature extraction techniques that can be extension of two dimensional feature extractions. At rst, 3D corner detector and 3D blob detector, two popular interest point detectors that successfully extended to spatio-temporal domain are reviewed. 3D corner detector is an extension of Harris corner detector which is proposed for corner detection in space-time and 3D blob detector is the 3D version of Hessian blob detector. A method is proposed to improve the robustness of 3D corner detectors against illumination variances based on the application of sigmoid function in contrast stretching. These interest points need to be described by feature vectors. In this part also a review on the SIFT descriptor and it's 3D extension is done. Another 3D feature descriptor discussed in this thesis is Cuboid. In the feature description part a new method is proposed based on the 3D SIFT feature descriptor by changing the binning method of the gradient orientation histogram to a non-uniform binning scheme. Using these non-uniform bins increases the accuracy of action classi cation by focusing on the areas near the interest points. Action classi cation is done using bag of features and supervised learning techniques. For making a dictionary of features, feature vectors are clustered by K-means clustering method and support vector machine is used for classi cation of human activities.