CAJ | 학술논문

大多数现有的基于稀疏表示的跟踪器仅采用单个目标特征来描述感兴趣的目标，因而在处理各种复杂视频时不可避免会出现跟踪不稳定的情况。针对这个问题，提出一种基于多特征联合稀疏表示的粒子滤波跟踪算法。该算法的主要思想是对随时间不断更新的字典模板和抽样粒子的局部块依据其位置进行分类，用字典中所有类别块对抽样粒子的局部块进行稀疏表示，而仅用与字典中具有相同类别的局部块及表示系数进行重构，根据重构误差构建似然函数以确定最佳粒子（候选目标），实现对目标的精确跟踪。该方法不仅实现了局部块的结构稀疏性，而且充分考虑了粒子之间的依赖关系，提高了跟踪精度。将算法进一步推广到采用基于核的多种特征描述，经混合范数约束并利用 KAPG （kernelizable accelerated proximal gradient ）方法求解联合特征的稀疏系数。定性和定量的实验结果均表明该算法在目标发生遮挡、旋转、尺度变化、快速运动、光照变化等各种复杂情况下，依然可以准确地跟踪目标。
대다수현유적기우희소표시적근종기부채용단개목표특정래묘술감흥취적목표，인이재처리각충복잡시빈시불가피면회출현근종불은정적정황。침대저개문제，제출일충기우다특정연합희소표시적입자려파근종산법。해산법적주요사상시대수시간불단경신적자전모판화추양입자적국부괴의거기위치진행분류，용자전중소유유별괴대추양입자적국부괴진행희소표시，이부용여자전중구유상동유별적국부괴급표시계수진행중구，근거중구오차구건사연함수이학정최가입자（후선목표），실현대목표적정학근종。해방법불부실현료국부괴적결구희소성，이차충분고필료입자지간적의뢰관계，제고료근종정도。장산법진일보추엄도채용기우핵적다충특정묘술，경혼합범수약속병이용 KAPG （kernelizable accelerated proximal gradient ）방법구해연합특정적희소계수。정성화정량적실험결과균표명해산법재목표발생차당、선전、척도변화、쾌속운동、광조변화등각충복잡정황하，의연가이준학지근종목표。
Most existing sparse representation based trackers only use a single feature to describe the objects of interest and tend to be unstable when processing challenging videos .To address this issue , we propose a particle filter tracker based on multiple feature joint sparse representation .The main idea of our algorithm is to partition each particle region into multiple overlapped image fragments . Eevery local fragment of candidates is sparsely represented as a linear combination of all the atoms of dictionary template that is updated dynamically and is merely reconstructed by the local fragments of dictionary template located at the same position . The weights of particles are determined by their reconstruction errors to realize the particle filter tracking .Our method simultaneously enforces the structural sparsity and considers the interactions among particles by using mixed norms regularization . We further extend the sparse representation module of our tracker to a multiple kernel joint sparse representation module which is efficiently solved by using a kernelizable accelerated proximal gradient (KAPG ) method . Both qualitative and quantitative evaluations demonstrate that the proposed algorithm is competitive to the state‐of‐the‐art trackers on challenging benchmark video sequences with occlusion ,rotation ,shifting and illumination changes .