K. Pradeep Reddy,G. Apparao Naidu,B Vishnu Vardhan,



Computer Vision,Human Action Recognition,Multiple Views,Self- Similarity Matrix,Gaussian,Gabor,Wavelet,Accuracy,


Multi-View Human Action Recognition, as a hot research area in computer vision, has many more applications in various fields. Despite its popularity, more precise recognition still remains a major challenge due to various constraints. Extracting the robust and discriminative feature from video sequence is a crucial step in the Human Action Recognition system. In this paper, a new feature extraction technique is proposed based on the integration of three different features such as intensity, Orientation and Contour features. Unlike the earlier approaches which applied feature extraction directly over actions videos, this approach applies the feature extraction only over key frames which are extracted from a large set of frames. The key frames selection is accomplished based on a new mechanism, called Gradient Self-Similarity Matrix (GSSM). GSSM is proposed as an extension to the most popular Self-Similarity Matrix (SSM) by evaluating the gradients of actions frames before SSM accomplishment. Once the key frames are extracted, the hybrid feature extraction mechanism is applied and the obtained features are processed for classification through Support Vector Machine Classifier. The proposed framework is systematically evaluated on IXMAS dataset and NIXMAS dataset. Experimental results enumerate that our method outperforms the conventional techniques in terms of recognition accuracy.


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