摘要
多数现有特征提取方法仅采用简单的形态特征,存在走与跑识别率较低的问题。将运动速度特征与较精确分割并归一化图像大小后的主分量分析外形特征相结合,采用支持向量机从8个方向对跑、蹲、站、弯腰、招手、指和走7种人体行为进行识别,结果证明走与跑的识别率得到很大提高。
Most of the existing characteristic extraction methods just use simple shape characteristics and exist problem of low walking and running recognition rate. This paper combines the velocity characteristics of movement and the Principal Component Analysis（PCA） shape characteristics obtained a^er more accurate segmentation and unifying the size of images. It uses Support Vector Machine（SVM） to recognize seven kinds of human behaviors including running, squat, standing, bending, waving, directing and walking from eight directions. Experimental results show that walking and running get higher recognition rate.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第5期193-195,共3页
Computer Engineering
基金
广西科技厅基金资助项目(桂科能063006-5G-4
桂科基0731020)
关键词
行为识别
计算机视觉
支持向量机
主分量分析
behavior recognition
computer vision
Support Vector Machine（SVM）
Principal Component Analysis（PCA）
