基于RGB-D图像特征的人体行为识别

针对现有的多模态特征融合方法不能有效度量不同特征的贡献度的问题,文中提出基于RGB-深度(RGB-D)图像特征的人体动作识别方法.首先获取基于RGB模态信息的方向梯度直方图特征、基于深度图像模态信息的时空兴趣点特征和基于关节模态信息的人体关节点位置特征,分别表征人体动作.采用不同距离度量公式的最近邻分类器对这3种不同模态特征表示的预测样本进行集成决策分类.在公开数据集上的实验表明,文中方法具有简单、快速,高效的特点.

Human Action Recognition Using RGB-D Image Features

Since the existing multi-modal feature fusion methods cannot measure the contribution of different features effectively, a human action recognition method based on RGB-depth image features is proposed. Firstly, the histogram of oriented gradient feature based on RGB modal information, the space-time interest points feature based on depth modal information, and the joints relative position feature based on joints modal information are acquired to express human actions, respectively. Then, nearest neighbor classifiers with different distance measurement formulas are utilized to classify prediction samples expressed by the three modal features. The experimental results on public datasets show that the proposed method is simple, fast and efficient.