CAJ | 학술논문

针对目标纹理变化、光照和位置变化较大时，跟踪不稳定、易丢失目标的问题，提出通过多示例学习的训练数据生成局部稀疏编码，建立对象的外观模型。首先，目标对象的局部图像块由过完备字典结合稀疏编码表示；其次，分类器学习稀疏编码进而识别背景中的目标；最后，将训练分类器得到的结果输入粒子滤波框架，进而预测目标状态随时间的变化。此外，为了减少字典更新和分类器累积误差形成的视觉漂移，采用弱分类器结合强分类器进行目标跟踪。
침대목표문리변화、광조화위치변화교대시，근종불은정、역주실목표적문제，제출통과다시례학습적훈련수거생성국부희소편마，건립대상적외관모형。수선，목표대상적국부도상괴유과완비자전결합희소편마표시；기차，분류기학습희소편마진이식별배경중적목표；최후，장훈련분류기득도적결과수입입자려파광가，진이예측목표상태수시간적변화。차외，위료감소자전경신화분류기루적오차형성적시각표이，채용약분류기결합강분류기진행목표근종。
When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algo?rithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an on?line algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL frame?work. First, local image patches of a target object are represented as sparse codes with an over complete dictionary. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a weak classifier with a strong classifier is proposed.