K Ruben Raju,Yogesh Kumar Sharma,Birru Devender,



Human action recognition,Gaussian,Gradient,Gabor,Kernel ,Discriminant Analysis,Support vector Machine,Recognition Accuracy,


Aimed at the problems of Intensity, Contour and orientation information, a Human Action Recognition (HAR) method based on Fused Feature Vector (FFV) is proposed in this paper. The FFV is constructed based on three different features such as Intensity features, Gradient features, and Orientation features. These three set of features are obtained through three different feature extraction methods based on Gaussian Filter, Gradient Filter and Gabor filter. Further to ensure optimal discriminant subspace, Kernel Discriminant Analysis is employed as a dimensionality reduction technique. Given the FFV of each action image, Support Vector Machine (SVM) is employed for classification. The proposed recognition model is evaluated systematically on the three public datasets such as KTH dataset, Weizmann dataset and the challenging UCF YouTube action dataset. Experimental results prove that our method outperforms the conventional approaches in terms of recognition accuracy.


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