以头部为基准的人体轮廓模型

为了提高以人体轮廓为主要特征对人体行为进行识别时的性能,提出一种以头部为基准的人体轮廓模型.采用无需重新初始化的level set方法提取视频序列图像中的人体轮廓,将人体轮廓纵横比最小的帧确定为关键帧.根据欧式距离局部极大原则确定关键帧中轮廓上的人体端点（头、手、脚等）,通过肤色模型确定头部位置并作为轮廓标记的基准点,建立以头部为基准的人体轮廓模型.基于该模型提取特征,采用支持向量机（SVM）对人体行为进行识别.以WEIZMANN数据库为对象的行为识别实验结果验证了该模型和行为识别方法的有效性.

Head-reference human contour model

A head-reference human contour model (HHCM) was proposed in order to improve the human motion recognition performance with human contour as the main feature. The human contour was extracted from the video frame sequence by employing the level set method without re-initialization, and the frame with the contour of the smallest aspect ratio was defined as the key frame. The contour endpoints in the key frame, i.e., the head, hands, and feet, etc were determined by the local maximum of Euclidean distances. The head location was obtained by the skin color model and was further considered as the base point of the contour. Then the head-reference contour model was established. Features were extracted based on the model, and support vector machine (SVM) was applied to identify human behaviors. The effectiveness of the model and the behavior recognition approach were verified by the experiments on the WEIZMANN database.