微型电脑应用2012，Vol.28Issue(4)：43-46,4.
低层次和高层次特征相结合的人体动作识别
Combination of Low-level and High-level Features for Human Action Recognition
摘要
Abstract
A new spatio-temporal interest point detector using 2D Gabor filters is presented to extract features of human action accurately, which is robust to occlusion, lighting changes and camera zooming. A polyhedron with eighty faces model-based spatio-temporal gradient descriptor is created to illustrate the spatio-temporal visual features of human action. A weight histogram is adopted as the action representation based on maximum likelihood estimation making the algorithm more efficient while the weight histogram is more discriminative. The low-level weight histogram and high-level semantic attributes are fused together and the latent Support Vector Machine (SVM) is adopted to find the local optimum of the prediction model. Experiments using some kinds of typical datasets demonstrated that approach achieves a higher recognition rate compared to existing methods.关键词
动作识别/时空兴趣点/时空梯度/最大似然/动作属性Key words
Action Recognition/ Spatio-temporal Interest Point/ Spatio-temporal Gradient/ Maximum Likelihood/ Action Attribute分类
信息技术与安全科学引用本文复制引用
王小念,姚莉秀..低层次和高层次特征相结合的人体动作识别[J].微型电脑应用,2012,28(4):43-46,4.
