摘要
利用行人头肩或者颜色特征,实现行人外层初定位;在初定位区域上,应用改进的尺度不变特征变换(SIFT)特征匹配实现对目标的精确定位.根据SIFT特征确定目标尺寸,解决行人尺度变化问题;将SIFT特征模板库更新机制引入特征保留优先级,解决行人短暂遮挡和形变的问题.为解决传统Cam-Shift算法的椭圆核函数自适应问题,将SIFT特征尺度变化与Epanechnikov函数融合,构成自适应带宽核函数,克服背景对目标的干扰.外层粗定位结果限制了Harris算子的检测范围,提高了SIFT特征匹配的实时性.实验结果证明,所提出移动机器人行人跟踪算法可以在目标尺度变化、短暂遮挡以及形变情况下实现行人跟踪.
First, the outer orientation of pedestrian target was obtained through head-shoulder or color fea-tures. Then the improved SIFT features of the pixels were extracted to locate the target accurately in the coarse location area. The pedestrian scale change problem was solved based on SIFT features to figure out the scale variation of the pedestrian. Simultaneously, the updating mechanism of SIFT features template library was introduced into feature reservation priority level to solve the problem of temporary occlusion and deformation of the target. Aiming at the adaptive elliptic kernel function of the traditional Cam-Shift algorithm, the variable-bandwidth and orientation ellipse kernel was combined with scale variations of the human and the Epanechnikov function, which reduced the interfere of background. Furthermore, the searching area for Harris operator was limited by the outler coarse location results, which improved the real-time performance of SIFT feature matching. The experimental results indicate that the proposed mo- bie robot human tracking algorithm can accomplish human tracking under conditions of target scale varia-tions, temporary occlusion and deformation.
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2016年第9期1677-1683,共7页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(61175087)
北京工业大学智能机器人"大科研"推进计划资助项目
