【摘要】 为了提高了人体行为识别的正确率,提出了一种基于改进Canny算子和神经网络的人体行为识别模型(ICanny-RBF)。采用改进Canny算子对人体行为图像进行预处理,提取人体行为轮廓,提取7个不变矩特征作为RBF神经网络的输入向量,训练出能够识别人体行为的RBF神经网络模型,并采用取k-means算法确定RBF神经网络聚类中心,采用Weizmann数据集进行仿真实验。仿真结果表明,与传统方法相比,提出的ICanny-RBF模型提高了人体行为的识别正确率。更多还原

【Abstract】 In order to improve the correct rate of recognition of human action, a human action recognition model is proposed based on improved Canny operator and neural networks in this paper. Improved Canny operator is used to preprocess the human action image to extract the contour of human action image, and then the 7 HU moment invariant features are extracted as input vector of RBF neural network and it trains to build RBF neural network model for the human action, and uses k-means algorithm to determine RBF neural network clustering center, simulation experiments are carried out on Weizmann data set. The simulation results show that the proposed model improves human action recognition correct rate and it is an efficient, high correct identification human activity recognition method.更多还原