南京理工大学学报（自然科学版）2018，Vol.42Issue(3)：271-277,7.DOI:10.14177/j.cnki.32-1397n.2018.42.03.003
基于结构化判别稀疏表示的目标跟踪
Structured discriminant sparse representation based object tracking
摘要
Abstract
An improved algorithm is proposed aiming at such shortcomings of sparse representation based object tracking algorithm as using an overall template and the poor ability of distinguishing targets from a background. Scale-invariant feature transform(SIFT)is used to extract the features of a target. Candidate objects are sparsely represented using appearance models of structured sparse representation,and sparse coefficients are obtained. A discriminant classifier is designed and trained by positive and negative samples,candidate objects are classified,and a confidence value is obtained. The tracking result of the previous frame is used to update the classifier and the dictionary. The improved algorithm is simulated. The average overlap ratio and average center point error of 3 test sequences of the simulation results are calculated,and Deer test sequence’s are 0.633 8 and 9.397 6, Car11 test sequence’s are 0.677 5 and 1.943 3,Caviar2 test sequence’s are 0.753 5 and 3.838 2.关键词
结构化稀疏表示/目标跟踪/尺度不变特征变换/分类器/字典Key words
structured sparse representation/object tracking/scale-invariant feature transform/classifiers/dictionaries分类
信息技术与安全科学引用本文复制引用
茅正冲,黄舒伟..基于结构化判别稀疏表示的目标跟踪[J].南京理工大学学报（自然科学版）,2018,42(3):271-277,7.基金项目
国家自然科学基金(60973095) （60973095）
江苏省产学研联合创新资金(BY2015019-29) （BY2015019-29）
