摘要
针对目标跟踪在遮挡和尺度变化等复杂背景下跟踪性能下降问题,联合稀疏约束、时间平滑约束以及增量投影非负矩阵分解,提出一种在线目标跟踪算法.首先利用非负矩阵分解学习一个基于部分表示的子空间,在此基础上添加稀疏约束提高处理遮挡能力,添加时间平滑约束提高算法的稳定性;然后用增量方式完成子空间的在线更新,减少算法计算量、提高外观模型更新效率;最后在粒子滤波框架下,以重构误差为基础改进了观测似然函数,将具有最大后验概率的候选目标作为目标在当前帧的图像区域.实验结果表明,在各种含有遮挡和尺度变化的视频中,该算法可以更稳定地跟踪目标.
The performance of existing object tracking algorithms decreases obviously when the object undergoes partial or full occlusion,and posture change with complex background.To handle these problems,an online incremental projection non-negative matrix factorization based object tracking algorithm with sparse and time smooth constraints is proposed.The local structure of the target is represented by the basis matrix,which is extracted by non-negative matrix factorization along with the sparse constraint to deal with a variety of challenging scenarios and the time smooth constraint to improve the tracking robustness.The incremental basis matrix updating strategy reduces the amount of computation evidently,resulting in the appearance model updating more efficiently.In the particle filter framework,the observation likelihood function is modified based on the reconstruction error of candidates when projected to the basis matrix,the candidate with a max posteriori probability is recognized as the target in the current frame.Experimental results on various video sequences show that,compared with the state-of-the-art tracking methods,the proposed algorithm achieves favorable performance when the object has large occlusion or scale variation.
作者
王华彬
田猛
周健
施汉琴
陶亮
Wang Huabin;Tian Meng;Zhou Jian;Shi Hanqin;Tao Liang(Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230039;Institute of Media Computing, Anhui University, Hefei 230601)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2017年第9期1658-1666,共9页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61372137
61301295)
安徽省自然科学基金(1308085QF100
1408085MF113)
安徽大学信息保障技术协同创新中心开放课题(ADXXBZ201411)
安徽大学博士科研启动基金
关键词
非负矩阵分解
稀疏约束
平滑约束
局部特征
粒子滤波
non-negative matrix decomposition
sparse constraint
smooth constraint
local characteristics
particle filter
