基于ISA网络的视频人体行为分类识别

Video human behavior recognition based on ISA network model

采用基于独立子空间分析(ISA)模型与神经网络理论形成的ISA深度网络模型特征提取方法,并结合数据预处理方法、K-means聚类方法以及支持向量机(SVM)分类器等实现对视频人体行为的分类识别.将本研究方法应用到Hollywood2人体行为数据集上进行人体行为识别实验,并与其他常用人体行为特征提取和识别方法进行对比,实验结果验证了本研究方法在人体行为分类识别中的有效性.

By using the independent subspace analysis (ISA) deep network model feature extraction method,which is based on the ISA model and neural network theory,video classification and identification of human behavior was achieved,in which the data preprocessing methods,K-means clustering methods,and support vector machine (SVM) classifiers were combined.The experiment was conducted on the basis of the Hollywood2 human behavior data set.This experiment was compared with other commonly used human behavior feature extraction and recognition methods.The experimental results validate the effectiveness and advantages of this method in the classification and recognition of human behavior.