摘要
针对目前的稀疏表示目标跟踪算法实时性差的问题,提出了融合背景信息的分块稀疏表示目标跟踪算法.该算法首先在粒子滤波框架下通过快速算法筛选候选目标,然后采用图像分块并给每个分块赋予不同权重的方法解决部分遮挡和噪声干扰等问题,最后将背景信息融入对象字典,并通过目标与背景信息联合表示方法计算目标的稀疏解.在Matlab平台上将文中算法与另外2种算法于4组视频中进行比较,结果表明,文中算法具有运算速度快和鲁棒性强的特点.
As the current sparse representation tracking algorithms are of low real-time performance,this paper puts forward a tracking algorithm of the block sparse representation with background information.In the algorithm,first,candidate targets are selected through a fast algorithm in the framework of particle filtering.Then,images are divided into blocks with different weights to reduce the disturbance of occlusions and noises.Finally,the background information is fused into the template dictionary,and the sparse solution of the candidate targets is calculated through the collaborative representation of the targets and the background.The proposed algorithm is compared with the other two algorithms by using four videos on the Matlab platform.The results show that the proposed algorithm is of a higher calculation speed and a stronger robustness.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第8期21-27,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
粤港关键领域重点突破项目(2011BZ100012)
关键词
目标跟踪
稀疏表示
模板字典
粒子滤波
object tracking
sparse representation
template dictionary
particle filtering
