一种基于属性贝叶斯网络的行为识别模型

On a Human Behaviors Classification Model Based on Attribute-Bayesian Network

针对传统行为识别方法仅利用底层特征识别的不足,提出了一种将动作属性与贝叶斯网络相结合的行为识别方法.首先,提取视频中的时空兴趣点及其3D-SIFT特征描述符,用词袋的方法建立时空词典对视频序列进行表示;然后,利用底层特征训练属性分类器,构造由底层特征到高层特征的映射,将底层特征样本经过属性分类器后得到行为—属性的样本信息,并采用MAP(最大后验概率)准则学习贝叶斯网络结构,从而建立一种基于属性贝叶斯网络的行为识别模型.实验结果表明该模型能有效地进行行为识别.

Due to defect of only using low-level features in the traditional recognition methods,this paper proposes a novel method on action recognition by combining Bayesian Network model with high-level se-mantic concept （human action attribute）.Firstly,we have extracted spatio-temporal interest points and 3D-SIFT descriptors around each interest point in the videos.Bag of words as low-level features have been used to describe these videos before building the proj ection from low-level features to high-level features through trained attribute-classifiers.Finally,the Attribute-Bayesian network structure has been studied based on Maximum a Posterior Probability （MAP）mechanism.The experimental results illustrate that the model is effective on action recognition.