为克服方向梯度直方图(HOG)特征在人体行为识别中仅表征动作的全局梯度特征、缺乏局部细节信息、对遮挡表现不佳的问题,改进了一种基于全局特征和局部特征的方法来实现人体行为识别。该算法首先使用背景减法获得人体运动区域;方向可控滤波器能有效描述动作边缘特征,通过引入方向可控滤波器改进HOG特征以增强局部边缘信息,同时对加速稳健特征进行k-means聚类获得词袋模型;最后将融合后的行为特征输入支持向量机对行为特征进行分类识别。在数据集KTH、UCF Sports和SBU Kinect Interaction上进行仿真模拟,结果表明改进的算法识别准确率分别达到了96.7%、94.2%和90.8%。

This paper improves a global and local feature-based method to overcome problems of the histogram of oriented gradients (HOG), such as the features only characterizing the global gradient feature of motion, lacking local detail information, and having poor performance on occlusion, in the human behavior recognition. The proposed algorithm first uses the background difference method to obtain the human motion region; then, a steerable filter can effectively describe the motion edge features to improve HOG features,therefore enhancing edge details. At the same time, k-means clustering is conducted on speeded up robust features (SURF) to obtain the bag-of-words model. Finally, the merged behavior features are input into a support vector machine (SVM) for classification and recognition. Simulation experiments perform on the KTH, UCF Sports, and SBU Kinect Interaction datasets, showing improved algorithm recognition accuracies of 96.7%, 94.2%, and 90.8%, respectively.