Highly Efficient Human Action Recognition with Quantum Genetic Algorithm Optimized Support Vector Machine
This work addresses action recognition for applications like surveillance or human-computer interaction, but it is incremental as it combines existing methods (quantum genetic algorithm and SVM) with a new feature extraction approach.
The paper tackled human action recognition by optimizing a support vector machine with a quantum genetic algorithm, achieving 96.15% accuracy on the MSR-12 dataset, outperforming a conventional SVM at 93.85%.
In this paper we propose the use of quantum genetic algorithm to optimize the support vector machine (SVM) 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, including the optimized SVM based on quantum genetic algorithm and the conventional SVM with grid search. Experimental results on the MSR-12 dataset show that the conventional SVM achieved an accuracy of $ 93.85\% $. The proposed approach outperforms the conventional method with an accuracy of $ 96.15\% $.