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During action recognition process under the mobile phone visual sensor, in order to ensure the speed of recognition, generally distributing as sloppy feature points, so as to express composite action feature needs to be recognized. However, if the action is composite feature in small area, traditional action recognition method based on sparse representation, when expressing action, is not able to express feature details within small area in high accuracy and achieve action recognition under mobile phone visual sensor accurately and effectively. To this end, an action recognition method under mobile phone visual sensor based on visual optimization analysis of behavior is proposed. With the maximum similarity clustering method to segment action images obtained by mobile phone vision sensor accurately, to exclude a large number of irrelevant features. By fitting the action clustering function to calculate principal curvature of action edge position, so as to acquire the position and scale of action. By adopting the action gray level co-occurrence matrix to apply adaptive classification for action, quickly complete the accurate identification of the action under mobile phone visual sensor. The simulation results show that, this algorithm has high stability and accuracy. Introduction With the development of computer hardware technology, human-computer interaction is developing toward various novel interaction ways [1,2]. Action as a way of interaction consistent with the living habits of people, depending on the characteristics like straight forward and visual, regarded as a very important part of human-computer interaction [3,4]. Therefore, action recognition gets more and more extensive attention, becomes the key topic for the related scholars to study [5]. During action recognition process under the mobile phone visual sensor, the process is vulnerable to the interference of objects with similar skin color and action, in the process of recognizing the action, in order to ensure the speed of recognition, generally distributing as sloppy feature points, so as to express composite action feature needs to be recognized[6,7]. However, if the action is composite feature in small area, traditional action recognition method based on sparse representation, when expressing action, is not able to express feature details within small area in high accuracy and achieve action recognition under mobile phone visual sensor accurately and effectively. Aiming at the shortcoming of the traditional algorithm, an action recognition method under mobile phone visual sensor based on visual optimization analysis of behavior is proposed. With the maximum similarity clustering method to segment action images obtained by mobile phone vision sensor accurately, to exclude a large number of irrelevant features. By fitting the action clustering function to calculate principal curvature of action edge position, so as to acquire the position and scale of action. By adopting the action gray level co-occurrence matrix to apply adaptive classification for action, quickly complete the accurate identification of the action under mobile phone visual sensor. The simulation results show that, this algorithm has high stability and accuracy. International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2015) © 2015. The authors Published by Atlantis Press 1192 Action recognition principle based on sparse representation under mobile phone vision sensor L1-LS algorithm is used to solve the sparse representation, this algorithm solves the i norm minimization problem by least square method. Least square method have optimal statistics for normally distributed data, when the least square method is selected, usually the simplex method is adopted to complete the calculation of linear programming and the selecting weight iteration. Assuming that training samples consisted of the action image under mobile phone visual sensor is ik i d k v = − , wherein, i v , which is used to describe the input value of the training samples in the category i , subsets ( ) ( ) ( ) ( )2/ ( 1) 1 1 , , / t ik c m t t ik jk j i k d d m − = = ∀ ∑ is used to linearly describe the new sample

Research on action recognition method under mobile phone visual sensor