CAJ | 학술논문

目标表观变化的处理是视觉跟踪领域极具挑战性的问题，该文针对这一问题，在粒子滤波框架下提出一种高效的基于超像素的L1跟踪方法(SuperPixel-L1 tracker, SPL1)。首先利用具有结构性信息的中层视觉线索(超像素)构造字典来对目标的表观建模；然后求解由粒子表示的候选目标状态的 L1范数最小化，把重构误差最小的候选状态作为跟踪的结果；最后进一步改进了字典的在线更新策略，不论目标发生遮挡与否，字典都被学习更新；为了降低目标发生漂移的可能，更新时保留初始帧的信息。仿真结果验证了SPL1在目标发生长时间遮挡、尺度和光照变化时依然能够稳定地跟踪目标。
목표표관변화적처리시시각근종영역겁구도전성적문제，해문침대저일문제，재입자려파광가하제출일충고효적기우초상소적L1근종방법(SuperPixel-L1 tracker, SPL1)。수선이용구유결구성신식적중층시각선색(초상소)구조자전래대목표적표관건모；연후구해유입자표시적후선목표상태적 L1범수최소화，파중구오차최소적후선상태작위근종적결과；최후진일보개진료자전적재선경신책략，불론목표발생차당여부，자전도피학습경신；위료강저목표발생표이적가능，경신시보류초시정적신식。방진결과험증료SPL1재목표발생장시간차당、척도화광조변화시의연능구은정지근종목표。
Handling appearance variations is a very challenging issue for visual tracking. In this paper, an effective superpixel based L1 tracking method (SuperPixel-L1 tracker, SPL1) is proposed to deal with the above problem in a particle filter framework. First, the mid-level visual cue with structural information is exploited to construct the dictionary and model the object appearance. Then each candidate state defined by a particle is solved via L1 minimization. The candidate with the smallest reconstruction error is selected as the tracking result. Finally, the online dictionary updating strategy is further improved. The dictionary needs to be updated regardless of whether the object is occluded or not. The initial frame information is retained during the updating process to reduce the possibility of the object drift. Simulation results show that SPL1 tracker can still stably track the object under the circumstance of long-term occlusion, large scale and illumination changes.