基于深度信息的人体运动识别方法

近年来，可见光视频序列的人体运动识别研究已经取得了一定的进展。由于这些数据源容易受到目标颜色、光照强度和背景杂波的影响，因此将深度信息应用于人体运动识别。本文首先采用了基于时空兴趣点的人体运动的局部表征方法，分别实现了Harris时空兴趣点与基于Gabor滤波器的时空兴趣点(STIPs)检测方法在深度信息上的应用。然后对相应结果进行立方体描述并提取了深度立方体相似特征(DCSF)。最后利用基于时空码本的支持向量机(SVM)动作分类器完成对动作的分类。实验表明，基于Gabor滤波器的检测方法在深度数据集上取得了更好的识别效果。

Human motion recognition method based on depth information

In recent years, recognition of human motion on visible light video sequences has made some progress. Since the data sources are sensitive to target color, light intensity and background clutters, the depth information is applied to human motion recognition. In this paper, a local representation method of human movement based on Spatio-Temporal Interest Points(STIPs) is adopted, and the applications of Harris and Gabor filter detection methods on depth information are achieved. A novel Depth Cuboid Similarity Feature(DCSF) is built to describe the corresponding results. Finally, action classification is completed by Support Vector Machine(SVM) classifier based on spatio-temporal codebook. Experimental results demonstrate that detection method of Gabor filter obtains better recognition performance in depth datasets.