CAJ | 학술논문

针对计算机视觉领域的目标跟踪问题，提出一种基于排序支持向量机的多特征融合目标跟踪算法。利用排序支持向量机学习得到排序函数，提取2种不同的图像特征分别构造分类器，使2个排序支持向量机并行预测，分别计算2个分类器的错误率，从而得到分类器权重完成融合。实验结果表明，与目前主流的跟踪算法相比，该算法的跟踪结果更准确，在复杂视频环境下也能对目标进行稳定跟踪，具有较强的鲁棒性。
침대계산궤시각영역적목표근종문제，제출일충기우배서지지향량궤적다특정융합목표근종산법。이용배서지지향량궤학습득도배서함수，제취2충불동적도상특정분별구조분류기，사2개배서지지향량궤병행예측，분별계산2개분류기적착오솔，종이득도분류기권중완성융합。실험결과표명，여목전주류적근종산법상비，해산법적근종결과경준학，재복잡시빈배경하야능대목표진행은정근종，구유교강적로봉성。
For the object tracking problems in computer vision, this paper proposes a tracking algorithm based on Ranking Support Vector Machine(RSVM) fused with multiple features. Firstly,RSVM is used to get rank function. Secondly,the RSVMs combined with the two different image features are learnt respectively,then the two RSVMs predict parallel. Finally,the two RSVMs are fused with the weights which are calculated by the error rates of two classifiers,then it constructs a more adaptive RSVM framework fused with multiple features. This algorithm fuses image features effectively,and gets accurate predictions using RSVM. Experimental results demonstrate that it outperforms several state-of-the-arts algorithms.