CAJ | 학술논문

在复杂背景下，传统轮廓跟踪方法会发生漂移，甚至丢失目标。针对上述问题，提出一种基于局部模型匹配（LMM）的目标轮廓跟踪算法。利用超像素技术结合EMD相似性度量构建局部特征模型，从而进行局部模型匹配。结合粒子滤波的Snake模型作提取目标轮廓，实现目标轮廓精确跟踪。实验结果表明，该算法在目标形变、部分遮挡、复杂背景等条件下均具有较高的跟踪成功率。与多种目标轮廓跟踪算法进行对比，该算法具有较高的准确性和鲁棒性。
재복잡배경하，전통륜곽근종방법회발생표이，심지주실목표。침대상술문제，제출일충기우국부모형필배（LMM）적목표륜곽근종산법。이용초상소기술결합EMD상사성도량구건국부특정모형，종이진행국부모형필배。결합입자려파적Snake모형작제취목표륜곽，실현목표륜곽정학근종。실험결과표명，해산법재목표형변、부분차당、복잡배경등조건하균구유교고적근종성공솔。여다충목표륜곽근종산법진행대비，해산법구유교고적준학성화로봉성。
The traditional tracking methods make moving target easily drift, even result into the loss of the moving target under the complex background. Focusing on these problems, it proposes a object contour tracking algorithm based on Locally Model Matching(LMM). It uses super-pixel combining with the EMD similarity measure to build locally features model. It carries on locally model matching. The Snake model combines particle filter which extracts the target contour to achieve contour tracking accurately. The experimental results indicate that the proposed moving target tracking method maintaines a higher success rate that is under the conditions of the target deformation, partial occlusion and complex background. Compared with other moving target contour tracking methods, the proposed algorithm possesses accurateness and robustness.