CAJ | 학술논문

目标跟踪是计算机视觉领域的一个具有挑战性的问题，本文提出了一种基于ML （最大似然）估计和L2范数的视频目标跟踪算法。建立基于稀疏限制的ML模型，给样本中的异常像素分配较小的权值，减少异常像素对跟踪算法的影响。利用L2范数最小化进行稀疏编码求解。采用贝叶斯估计得出目标跟踪结果。与其他典型算法相比，本算法降低了计算的复杂度，对遮挡，旋转，尺度变化，光照变化等异常变化具有较强的鲁棒性。
목표근종시계산궤시각영역적일개구유도전성적문제，본문제출료일충기우ML （최대사연）고계화L2범수적시빈목표근종산법。건립기우희소한제적ML모형，급양본중적이상상소분배교소적권치，감소이상상소대근종산법적영향。이용L2범수최소화진행희소편마구해。채용패협사고계득출목표근종결과。여기타전형산법상비，본산법강저료계산적복잡도，대차당，선전，척도변화，광조변화등이상변화구유교강적로봉성。
The tracking of target is a challenging issue in computer vision .In this paper ,we propose a visual object tracking algorithm based on ML estimation and L2-norm .Firstly ,the model of sparsity constrained ML is established .Abnormal pixels in the samples will be assigned with low weights to reduce their affects on the tracking algorithm .Then ,L2-norm minimization is used to solve the sparse coding .Finally ,the object tracking results is obtained using Bayesian MAP estimation .Compared with other popular methods ,our proposed method reduces the computational complexity and has stronger robustness to abnormal changes (e .g .occlu-sion ,rotation ,scale change ,illumination ,etc .)