Detecting Remarkable Motion Feature for Action Recognition with SVM based on Kernel Parameters Optimization

摘要

This paper proposes an algorithm of knowledge discovery for remarkable motion features in daily life action recognition based on SVM. The main characteristics of the proposed method are 1) basic scheme of the algorithm is based on Support Vector Learning and its generalization error, 2) detection of remarkable motion features is done in response to kernel parameters optimization via minimization of generalization error. Experimental result shows that the proposed algorithm makes the accurate rate of the recognition system to be high and enables us to detect remarkable motion features intuitively.