CAJ | 학술논문

针对目标在复杂环境下容易受到外界干扰而发生漂移的问题，提出了一种基于超像素的局部判别式跟踪方法。首先，对视频序列前10帧的目标区域进行分割，得到超像素，并利用k-means方法对其进行聚类以构造初始字典；其次，通过训练样本集来训练线性分类器；然后，为了减少目标发生漂移的可能性，将初始训练的分类器与更新后的分类器线性加权之和定义为似然函数；最后，在粒子滤波的框架下，将似然函数值最大的粒子作为跟踪的结果，每运行 U帧更新一次字典和分类器参数，以捕获目标表观的变化。仿真结果表明，所提算法在目标发生遮挡、光照变化的复杂环境下仍然能够跟踪目标。
침대목표재복잡배경하용역수도외계간우이발생표이적문제，제출료일충기우초상소적국부판별식근종방법。수선，대시빈서렬전10정적목표구역진행분할，득도초상소，병이용k-means방법대기진행취류이구조초시자전；기차，통과훈련양본집래훈련선성분류기；연후，위료감소목표발생표이적가능성，장초시훈련적분류기여경신후적분류기선성가권지화정의위사연함수；최후，재입자려파적광가하，장사연함수치최대적입자작위근종적결과，매운행 U정경신일차자전화분류기삼수，이포획목표표관적변화。방진결과표명，소제산법재목표발생차당、광조변화적복잡배경하잉연능구근종목표。
To solve the drifting problem of objects caused by external disturbances under complex circumstances,a local discriminative tracking method based on superpixel is proposed.First,the ob-jects from the first ten frames of a video are segmented into superpixels,which are clustered by the k-means algorithm to construct the initial dictionary.Secondly,a linear classifier is trained by the training sample set.Then,to reduce the possibility of the object drifting,the likelihood function is defined as a linear weighted combination of the initial classifier and the updated classifier.Finally, under the particle filter framework,the particle with the highest likelihood confidence is considered as the tracking result.The dictionary and the parameters of the classifier are updated once every U frames to capture the variation of the object appearance.The simulation results show that the pro-posed algorithm can track the object under the complex circumstance with object occlusion and illu-mination change.