结合均值偏移和多特征的自动人头识别

Automatic head recognition by integrating mean shift with multiple features

为提高固定单目垂直摄像方式下人头目标识别的正确率,提出一种新的头部目标区域获取方法。首先给出基于Mean-shift的人头目标分割算法,由于综合考虑了像素点在空间信息和色彩信息的联系,能够较为完整地分割出人头部目标候选区域。在此基础上,基于运动人头区域的轮廓具有近似圆形以及人头发色具有聚类性2个关键特征,提出并建立了基于发色信息的头部区域评价模型和基于连通域边缘轮廓的头部目标评价模型来实现人头部目标区域的识别。实验结果表明,提出的算法能有效抑制光照的影响和消除与发色分布类似的伪目标,静态图像检测正确率约为89.4%。

To improve the head detection accuracy in video sequences captured with fixed vertical monocular camera, a novel method of head recognition based on mean shift and multiple features is proposed. Firstly, mean shift-based image segmentation algorithm with color information and spatial information is suggested to derive the candidate head components in images. Furthermore, by referring to two features that the contour of human head regions are approximate round and the hair color distribution is clustered, the evaluation models based on the contour information and inside color information of candidate head components are presented for head recognition. The experimental results show that the proposed algorithm can effectively reduce the light interfere and eliminate fake target whose color information is similar to hair color distribution. The detection rate for static images can reach about 89.4%.