CAJ | 학술논문

结合正负样本相互作用思想和随机森林算法构建检测器，融合基于LK光流法的跟踪器，提出一种基于TLD( Tracking Learning Detecting)的随机森林长期目标跟踪方法。将该方法与Mean-Shift算法、TLD算法进行对比，结果表明该算法能很好应对目标丢失、遮挡情况，准确率在93%以上。在多种情况下对该方法进行实验验证，可实现刚性物体和非刚性物体在复杂背景下的长时间精确跟踪。
결합정부양본상호작용사상화수궤삼림산법구건검측기，융합기우LK광류법적근종기，제출일충기우TLD( Tracking Learning Detecting)적수궤삼림장기목표근종방법。장해방법여Mean-Shift산법、TLD산법진행대비，결과표명해산법능흔호응대목표주실、차당정황，준학솔재93%이상。재다충정황하대해방법진행실험험증，가실현강성물체화비강성물체재복잡배경하적장시간정학근종。
In this paper, we propose a target tracking method based on Tracking Learning Detec-ting ( TLD) random fores by using the detector constructed by the ideas of the interaction be-tween positive and negative samples and random forest algorithm, and tracker based on LK opti-cal flow method. This method is performed the comparison with the Mean Shift algorithm and TLD method. The results show that the algorithm can have the strong robustness to target lost, target occlusion, and the accuracy rate is more than 93%. The experiment results in many cases verify that this method can achieve a long time accurate tracking for rigid and non-rigid object in complex background.