摘要
行为分类中,现有的特征提取要么方法简单、识别率低,要么特征提取复杂、实时性差。对此,提出一种算法:将步态能量图(GEI)改进,得到增强步态能量图(EGEI);然后将二维保局映射(2DLPP)应用于特征空间降维;最后采用最近邻(NN)法分类。EGEI比GEI更能反映目标特征;2DLPP降维效果好于主成分分析(PCA)及一维保局映射。在Weizmann行为数据库上测试,实验结果表明:该算法简单、准确率高,平均识别率达到了91.22%。
In action classification, methods of feature extraction were either simple with low accuracy, or complicated with poor real-time quality. To resolve this problem, firstly, Enhanced Gait Energy Image （EGEI） was derived from Gait Energy Image （GEl） ; secondly, high dimensional feature space of the action was reduced to lower dimensional space by Two- Dimensional Locality Preserving Projection （2DLPP）; then Nearest-Neighbor （NN） classifier was adopted to distinguish different actions. EGEI could extract more obvious feature information than GEl; 2DLPP outperformed principal component analysis and locality preserving projections in dimensional reduction. It was tested on the Weizmann human action dataset. The experimental results show that the proposed algorithm is simple, achieves higher classification accuracy, and the average recognition ratio reaches 91.22%.
出处
《计算机应用》
CSCD
北大核心
2011年第3期721-723,744,共4页
journal of Computer Applications
基金
国家863计划项目(2008AA01Z148)
黑龙江省杰出青年科学基金资助项目(JC200703)
关键词
行为识别
智能监控
特征提取
增强的步态能量图
二维保局映射
action recognition
intelligent supervision
feature extraction
Enhanced Gait Energy Image （EGEI）
Two- Dimensional Locality Preserving Projection （2DLPP）
