基于星形距离和LDCRF模型的在线行为识别方法  

Online behavior recognition based on star distance and LDCRF model

作　　者：成立[1] 梅雪[1] 张玉燕[1] 马士林[1] 袁宇浩[1] 施晓东[2] 

机构地区：[1]南京工业大学自动化与电气工程学院,江苏南京210000 [2]哈尔滨工业大学电气工程学院,黑龙江哈尔滨150001

出　　处：《计算机工程与设计》2015年第6期1626-1629,1636,共5页Computer Engineering and Design

基　　金：2012年度江苏省"青蓝工程"中青年学术带头人基金项目

摘　　要：提出一种基于星形距离轮廓特征和LDCRF模型的在线行为识别方法。对视频中已分割出的人物姿态提取轮廓,求取人体轮廓质心及其到轮廓采样点的星形距离向量,以该向量参数化人体运动姿态特征,对原始姿态特征向量进行小波变换,降维的同时获得姿态的多分辨细节信息。利用潜动态条件随机场模型(latent-dynamic conditional random,LDCRF)对人体行为特征建模,进行在线识别。比对CRF、HCRF、LDCRF模型对10种不同行为的识别结果,对比结果表明,相比CRF和HCRF,该模型对连续行为序列有较强的识别能力,具有更好的稳定性。The online behavior recognition based on star distance and LDCRF model was presented .The contour for the segmen‐tation of character gesture in the video was extracted ,and the human movement characteristics were parameterized through the calculated star distance between body contours centroid and sampling point ,then its dimensionality was reduced using wavelet transform .A model for human behavior characteristics was built by using LDCRF and the behavior was recognized .During the simulation experiment ,by comparing the identification results of CRF ,HCRF and LDCRF of ten different behaviors respective‐ly ,the model LDCRF is verified to be better in recognizing continuous behavior sequence than CRF and HCRF .Moreover ,it has better stability .

关 键 词：行为识别 星形距离 潜动态条件随机场 行为建模 小波变换 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]