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智能步态门禁系统实验平台的设计
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格式:rar |
大小:243 |
2009-08-10
王敏
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为探索步态识别技术在实际中的应用，构建了基于步态图像特征进行身份识别的智能
门禁系统硬件平台，且开发了相应的门禁系统软件。其技术流程为：首先从摄像机捕获视频输入计算机进行监控，发现后对人体进行检测与跟踪，分割人体轮廓并将其规格化叠加处理获取步态特征图，然后提取包含人体整体模型信息的边界矩参数，以此作为识别参量输入支持向量机（SVM）进行分类识别。为评价步态识别性能，分别对正确识别率（PCR）、错误接受率（FAR）和错误拒绝率（FRR）进行了测试，结果表明该实验平台能较好地对人体目标进行自动监控跟踪、提取步态特征且有效地进行分类识别。
关键词：步态识别 门禁系统 特征提取 不变矩
Abstract: In order to explore the practical application of gait recognition, this experimental
platform of access control system based on gait feature was built for human recognition. The
software of experimental platform was compiled. This study deals with the following process. The video frequency was collected from the camera and put into computer. Then the object was detected and also tracked. Body silhouette sequences of gait were extracted and normalized in this study. The sequences were added together and gait character image could be obtained. At the same time moment invariants was used in extracting gait character parameters involving integrated model. The technique of support vector machines (SVM) was presented for gait recognition. In order to test the performance of gait recognition, three items were performed for Probability of Correct Recognition (PCR), False Rejection Rate (FRR) and False Acceptance Rate (FAR).
According to the results, it proves that the experimental platform was beneficial for watching the
object and at the same time tracking the human body. It also revealed great value for feature
extract and gait classification.
Keywords: Gait recognition; Access control system; Feature extract; Invariant Moments
门禁系统硬件平台，且开发了相应的门禁系统软件。其技术流程为：首先从摄像机捕获视频输入计算机进行监控，发现后对人体进行检测与跟踪，分割人体轮廓并将其规格化叠加处理获取步态特征图，然后提取包含人体整体模型信息的边界矩参数，以此作为识别参量输入支持向量机（SVM）进行分类识别。为评价步态识别性能，分别对正确识别率（PCR）、错误接受率（FAR）和错误拒绝率（FRR）进行了测试，结果表明该实验平台能较好地对人体目标进行自动监控跟踪、提取步态特征且有效地进行分类识别。
关键词：步态识别 门禁系统 特征提取 不变矩
Abstract: In order to explore the practical application of gait recognition, this experimental
platform of access control system based on gait feature was built for human recognition. The
software of experimental platform was compiled. This study deals with the following process. The video frequency was collected from the camera and put into computer. Then the object was detected and also tracked. Body silhouette sequences of gait were extracted and normalized in this study. The sequences were added together and gait character image could be obtained. At the same time moment invariants was used in extracting gait character parameters involving integrated model. The technique of support vector machines (SVM) was presented for gait recognition. In order to test the performance of gait recognition, three items were performed for Probability of Correct Recognition (PCR), False Rejection Rate (FRR) and False Acceptance Rate (FAR).
According to the results, it proves that the experimental platform was beneficial for watching the
object and at the same time tracking the human body. It also revealed great value for feature
extract and gait classification.
Keywords: Gait recognition; Access control system; Feature extract; Invariant Moments
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