论文标题
红外目标跟踪基于近端鲁棒主组件分析方法
Infrared target tracking based on proximal robust principal component analysis method
论文作者
论文摘要
红外目标跟踪在民事和军事领域都起着重要作用。为红外序列设计强大而高精度跟踪器的主要挑战包括重叠，遮挡和外观变化。为此，本文提出了基于近端鲁棒主组件分析方法的红外目标跟踪器。首先，将观察矩阵分解为稀疏的闭塞矩阵和低级别靶矩阵，并以接近的近端标准进行约束优化，该标准比L1-norm更好。为了解决此凸优化问题，采用乘数的交替方向方法（ADMM）交替估计变量。最后，利用具有模型更新策略的粒子过滤器框架来定位目标。通过对实际红外目标序列进行的一系列实验，证明了我们算法的有效性和鲁棒性。
Infrared target tracking plays an important role in both civil and military fields. The main challenges in designing a robust and high-precision tracker for infrared sequences include overlap, occlusion and appearance change. To this end, this paper proposes an infrared target tracker based on proximal robust principal component analysis method. Firstly, the observation matrix is decomposed into a sparse occlusion matrix and a low-rank target matrix, and the constraint optimization is carried out with an approaching proximal norm which is better than L1-norm. To solve this convex optimization problem, Alternating Direction Method of Multipliers (ADMM) is employed to estimate the variables alternately. Finally, the framework of particle filter with model update strategy is exploited to locate the target. Through a series of experiments on real infrared target sequences, the effectiveness and robustness of our algorithm are proved.
