摘要
在目标跟踪中,目标颜色变化或相似颜色背景干扰会导致算法的鲁棒性较差。为解决该问题,提出一种基于多级颜色累积和纹理融合的目标跟踪算法。将算法分为颜色与纹理特征提取、目标与背景特征相似度判断2个子过程,子过程交替执行,在目标颜色发生变化时,利用感兴趣区域帧差法对目标进行再定位,提取多级颜色模板,并将其累积在原模板之上。实验结果表明,该算法的平均跟踪误差约为基于颜色-纹理算法的1/2、为单视觉特征跟踪算法的1/3。
Aiming at several cases existing in the object tracking algorithm, such as failure happens when the object＇s appearance changes or when the target and background are similar, an object tracking algorithm based on multistep color accumulation and texture fusion is proposed in this paper. The whole tracking algorithm can be divided into two parts： color and texture feature extracting and target and background＇s similarity judging, in the tracking process those two parts run by turns according to different cases. When the object＇s appearance changes, the Region of Interest（ROI） frame difference is used to compute the center of the target again, extracts multistep color model and accumulats it to the old one. Experimental results show that the average tracking error of this algorithm is half of the algorithm based on color-texture and one third of single visual feature tracking algorithm.
出处
《计算机工程》
CAS
CSCD
2013年第7期209-213,共5页
Computer Engineering
基金
国家自然科学基金资助项目(61071168)
安徽省科技攻关计划科技强警专项基金资助项目(1101b0403030)
安徽大学青年科学研究基金资助项目(2009A1)
关键词
多级颜色累积
局部二值模式
相似度函数
感兴趣区域
多特征融合
目标跟踪
multistep color accumulation
Local Binary Pattem（LBP）
similarity function
Region of Interest（ROI）
multi-feature fusion
object tracking
