基于多任务结构稀疏表示的跟踪算法  

Tracking algorithm based on multi-task structure sparse representation

作　　者：王琳[1] 陈志国[1] 傅毅[1,2] WANG Lin;CHEN Zhi-guo;FU Yi(School of Internet of Things Engineering,Jiangnan University,Wnxi 214122,China;Wuxi Research Center of Environmental Science and Engineering,Wuxi 214153,China)

机构地区：[1]江南大学物联网工程学院,江苏无锡214122 [2]无锡环境科学与工程研究中心,江苏无锡214153

出　　处：《传感器与微系统》2018年第10期140-142,146,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(61502203);江苏省自然科学基金资助项目(BK20150122);江苏省高等学校自然科学研究面上资助项目(17KJB520039)

摘　　要：针对目标快速运动引起的模糊和空间结构信息利用不足的问题,提出一种多任务结构稀疏表示的跟踪算法。算法预先将每帧图像调整到固定尺寸,通过对样本进行重叠分块,充分利用样本的整体、局部和空间结构信息。利用对齐池算法提取各图像块对应的多任务结构稀疏表示的系数信息,判断候选样本与目标的相似性。标准测试集上的实验结果表明,所提算法在处理遮挡和快速运动等因素时更具鲁棒性,跟踪精度更高。Aiming at problem of blur caused by fast motion of target and lacking of spatial structural information usage,a tracking algorithm based on multi-task structure sparse representation is proposed. Each frame is adjusted to a fixed size in advance and the algorithm makes the most use of the whole,local and spatial structural information by the means of the overlapping patches over samples. Then,the similarity between the candidate samples and the target is determined by exploiting the alignment pooling algorithm,which can extract the coefficient information of the multi-task structure sparse representation corresponding to each image block. The experimental results on benchmark dataset show that the proposed method is more accurate and robust in dealing with fast motion and occlusion.

关 键 词：运动模糊 结构稀疏表示 对齐池算法 稀疏表示 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]