CAJ | 학술논문

　　针对视频序列图像目标跟踪中的光照变化问题，提出一种在粒子滤波器框架内，基于目标的局部二元模式(LBP)纹理特征，使用稀疏表达进行目标跟踪的方法。当前帧的跟踪粒子由前一帧的跟踪结果按高斯分布来生成。通过解l1正则化最小二乘方问题，获得每个粒子对应于模板子空间的稀疏表达，确定当前帧图像中的跟踪目标。然后使用粒子滤波器生成下一帧跟踪的粒子分布。在跟踪过程中采用新的动态模板更新策略更新模板空间中的模板。实验结果证明该方法的有效性和先进性。
　　침대시빈서렬도상목표근종중적광조변화문제，제출일충재입자려파기광가내，기우목표적국부이원모식(LBP)문리특정，사용희소표체진행목표근종적방법。당전정적근종입자유전일정적근종결과안고사분포래생성。통과해l1정칙화최소이승방문제，획득매개입자대응우모판자공간적희소표체，학정당전정도상중적근종목표。연후사용입자려파기생성하일정근종적입자분포。재근종과정중채용신적동태모판경신책략경신모판공간중적모판。실험결과증명해방법적유효성화선진성。
Aiming at the problem of illumination variation in the object tracking of video image sequence, an object tracking method which uses sparse representation in particle filter frame is proposed based on LBP textual feature of object. The tracking particles of the current frame are generated by the last tracking re-sult according to Gaussian distribution, the sparse representation of each particle to the template subspace is obtained by solving the l1-regularized least squares problem, and the tracking object is determined. Then, particle filter is used to propagate sample distribution in next tracking frame. In the procedure, the template is updated using a new template updating strategy. The experimental results validate the performance and advancement of the proposed method.