Highly Efficient Human Action Recognition with Quantum Genetic Algorithm Optimized Support Vector Machine
share
In this paper we propose the use of quantum genetic algorithm to optimize the support vector machine for human action recognition. The Microsoft Kinect sensor can be used for skeleton tracking, which provides the joints' position data. However, how to extract the motion features for representing the dynamics of a human skeleton is still a challenge due to the complexity of human motion. We present a highly efficient features extraction method for action classification, that is, using the joint angles to represent a human skeleton and calculating the variance of each angle during an action time window. Using the proposed representation, we compared the human action classification accuracy of two approaches, inclduing the optimized SVM based on quantum genetic algorithm and the conventional SVM with cross validation. Experiemental results on the MSR-12 data show a higher accuracy in quantum genetic algorithm optimized support vector machine.
READ FULL TEXT
