CAJ | 학술논문

为更有效地检测人体目标,弥补单一模型在步态特征提取中的不足,提出了基于双模型的步态特征精密提取方法,且构建了基于步态特征进行身份认证的门禁监控实验平台.首先从摄像机捕获步态视频输入计算机,发现人体目标后对其进行检测与跟踪;然后分割人体轮廓并将其规格化叠加处理获取步态特征图;为精确提取步态特征,将人体整体模型与简化模型相结合,提取步态参数作为识别参量输入支持向量机(SVM)进行分类识别.正确识别率(PCR)为77%-80%.结果表明该方法有助于步态特征的精密提取,且实验平台能较好地自动监控人体目标并进行身份认证.
위경유효지검측인체목표,미보단일모형재보태특정제취중적불족,제출료기우쌍모형적보태특정정밀제취방법,차구건료기우보태특정진행신빈인증적문금감공실험평태.수선종섭상궤포획보태시빈수입계산궤,발현인체목표후대기진행검측여근종;연후분할인체륜곽병장기규격화첩가처리획취보태특정도;위정학제취보태특정,장인체정체모형여간화모형상결합,제취보태삼수작위식별삼량수입지지향량궤(SVM)진행분류식별.정학식별솔(PCR)위77%-80%.결과표명해방법유조우보태특정적정밀제취,차실험평태능교호지자동감공인체목표병진행신빈인증.
To better detect human body and remedy defects of feature extraction with a single model, a method for precision extraction of gait feature based on double model was proposed to obtain gait parameters , and an access monitoring platform for identity authentication was presented. Firstly, the gait video captured by camera was input to the computer to detect target and monitor the access. Secondly, body silhouettes were extracted and normalized to obtain the gait feature image. Thirdly, the integral model was combined with the simplified model to extract gait parameters. Finally, the technique of support vector machines was presented for identity authentication. The probability of correct recognition (PCR) has a-chieved 77%-80% . This method based on integral model and simplified model is helpful for precision extraction of gait feature. The platform can automatically detect human body and provide a better way for identity authentication.