CAJ | 학술논문

近年来目标的稀疏表示已经广泛应用到视频跟踪中。文中提出了一种基于局部稀疏表示的鲁棒目标跟踪算法,目标的表示将局部信息考虑在内,并且做出了遮挡处理。为了在新的帧中跟踪到目标,每一个候选目标通过在线构建的过完备字典以及模板解l1/2最小化问题稀疏表示。文中用l1/2规范最小化来代替l0,而不是用l1规范最小化,通过解l1/2最小化问题,可以找到比解l1最小化更稀疏、更精确的解。此外,l1/2比l0更容易求解。目标稀疏表示后,通过在线学习的分类器将目标区分出来。实验结果表明,与现有的一些算法相比,该算法稳定性好,性能更优越。
근년래목표적희소표시이경엄범응용도시빈근종중。문중제출료일충기우국부희소표시적로봉목표근종산법,목표적표시장국부신식고필재내,병차주출료차당처리。위료재신적정중근종도목표,매일개후선목표통과재선구건적과완비자전이급모판해l1/2최소화문제희소표시。문중용l1/2규범최소화래대체l0,이불시용l1규범최소화,통과해l1/2최소화문제,가이조도비해l1최소화경희소、경정학적해。차외,l1/2비l0경용역구해。목표희소표시후,통과재선학습적분류기장목표구분출래。실험결과표명,여현유적일사산법상비,해산법은정성호,성능경우월。
Recently sparse representation has been widely used in video tracking. In this paper,propose a robust target tracking method based on local sparse representation,considering the local information for object representation and take occlusion into account. In order to track the target in a new frame,each target candidate is sparsely represented by over-complete dictionary online constructed and target templates solving a l1/2-norm minimization problem. In this algorithm,use l1/2-norm minimization to replace l0-norm minimization in-stead of l1-norm minimization. By solving l1/2-norm minimization,can find a sparser and more accurate solution than l1-norm minimi-zation,moreover,it is much easier to be solved than l0-norm minimization. After that,a classifier is learned to distinguish the target from the background. Experimental results show that this method has good stability and the performance is superior to the current algorithms.