基于量子遗传算法优化支持向量机的高效人体动作识别
Highly Efficient Human Action Recognition with Quantum Genetic Algorithm Optimized Support Vector Machine
摘要：
在本文中，我们提出了使用量子遗传算法来优化支持向量机（SVM）的人体动作识别。微软Kinect传感器可用于骨骼跟踪，提供关节的位置数据。然而，由于人体运动的复杂性，如何提取运动特征来表示人体骨骼的动态仍然是一个挑战。我们提出了一种高效的动作分类特征提取方法，即使用关节角度来表示人体骨架，并计算每个角度在动作时间窗口内的方差。使用所提出的表示，我们比较了两种方法，包括基于量子遗传算法的优化支持向量机和传统的支持向量机与网格搜索的人体动作分类精度。在MSR-12数据集上的实验结果表明，传统的SVM达到了$ 93.85\% $的精度。所提出的方法优于传统的方法与$ 96.15\% $的准确性。
英文摘要：
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\% $.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
