基于W2KPCA-KNN算法的人体异常行为识别  

Abnormal Human Behaviors Recognition Based on W2KPCA-KNN Algorithm

作　　者：楼中望[1] 姚明海[1] 瞿心昱[1] 阮涛涛[1] 朱晓明[1] 

机构地区：[1]浙江工业大学信息工程学院,杭州310023

出　　处：《计算机系统应用》2011年第2期157-160,共4页Computer Systems & Applications

基　　金：浙江省自然科学基金(20080376)

摘　　要：基于视觉的人体异常行为识别在特征提取时通常采用简单的形状运动信息或传统PCA方法,前者信息量不足而后者忽略了数据中的非线性信息,因此将核主成分分析(KPCA)运用于人体异常行为识别解决了以上问题。针对KPCA提取异常行为特征时存在的不足,提出了W2KPCA-KNN算法,即在特征提取和分类两个阶段均进行相应加权运算,在保留行为图像信息的基础上,提高了识别的精度,有效满足了异常行为识别系统的技术要求。通过实验比对可知该算法效果在特征提取和分类方面均优于传统核主成分分析法以及最近邻分类器。The recognition based on vision to extract features from abnormal human behaviors usually utilize straightforward sharp movement information or traditional PCA methods. The former lacks of information and the latter has ignored nonlinear information in data. Therefore, this paper will use KPCA in recognizing abnormal human behaviors to solve the aforementioned problems. Since KPCA has some defects in extracting feature abnormal behaviors, W2KPCA-KNN algorithm is proposed, which is to do weighting in both feature extraction and classification respectively. While retaining behavioral information in the image, it improves recognition accuracy and satisfies the technical requirements for abnormal behavior recognition system. The experimental comparisons show that this algorithm outperforms traditional KPCA and K-Nearest Neighbor classifier on both feature extraction and classification.

关 键 词：PCA KPCA KNN 人体异常行为 加权运算 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]