ID 原文 译文
24475 为提高复杂动态背景下运动目标检测精度，基于低秩及稀疏分解理论，本文提出一种基于群稀疏的运动目标检测方法。 To improve the accuracy of moving object detection under complex dynamic background, based on the theory of low-rank and sparse decomposition, a group sparse based moving object detection method is developed.
24476 所提方法将观测视频分解为低秩静态背景，群稀疏前景及动态背景三部分。 The proposed method decomposes the observed video into a low-rank static background, a group sparse foreground and a dynamic background.
24477 所提方法首先使用伽马范数近乎无偏近似矩阵秩函数，以解决核范数过度惩罚较大奇异值导致所得最小化问题无法获得最优解进而降低检测性能的问题； Regarding the problem that the nuclear norm over-penalizing large singular values leads to the optimal solution of the obtained minimization problem cannot be obtained and then the detection performance is decreased, the gamma norm is introduced to acquire almost unbiased approximation of rank function.
24478 其次，为利用前景目标边界先验信息以提升运动目标检测性能，每一帧使用过分割算法生成同性区域以定义群稀疏范数并用于约束前景矩阵； In order to utilize the object boundary prior to enhance the moving target detection performance, each frame is over-segmented into homogeneous regions which are taken to define the group sparse norm to constrain the foreground matrix.
24479 再次，为避免运动目标同时出现在稀疏前景和动态背景中，引入非相干项以提升二者可分性； Moreover, to prevent the moving object from appearing in the sparse foreground and dynamic background simultaneously, the incoherence term is introduced to enhance the separability of them.
24480 最后，本文利用交替方向乘子方法（Alternating Direction Method of Multipliers，ADMM）求解所得非凸目标函数。 Finally, the obtained non-convex objective function can be solved using the alternating direction multipliermethod (ADMM).
24481 实验结果表明，与现有主流运动目标检测算法相比，复杂动态背景下本文所提方法可较好抑制动态背景从而显著提高复杂运动背景下运动目标检测精度。 The experimental results show that, compared with the state-of-the-art moving target detection algorithms, the developed method can suppress the dynamic background considerably and then improve the accuracy of moving object detection significantly under complex dynamic background.
24482 针对一类广泛存在的分布式流水线和车辆运输集成调度问题(Distributed Permutation Flow-shop and Ve⁃hicle Transportation Integrated Scheduling Problem, DPFVTISP), 本文建立问题模型,并提出一种超启发式三维分布估计算法(Hyper-Heuristic three-Dimensional Estimation of Distribution Algorithm, HH3DEDA) 进行求解。 Aiming at a kind of widely existing distributed permutation flow-shop and vehicle transportation integrated scheduling problem (DPFVTISP), this paper establishes the problem model and proposes a hyper-heuristic three-dimensional estimation of distribution algorithm (HH3DEDA) to solve it.
24483 首先, 根据DPFVTISP的问题特性, 采用贪婪策略设计一种新颖的编解码规则。 Firstly, a novel coding and decoding rules adopting the greedy strategy is designed via analyzing the characteristics of DPFVTISP.
24484 其次, 为实现对 DPFVTISP 问题解空间中不同区域的深入搜索, 设计 10种低层启发式操作 (即 10种有效的邻域操作), 并将其所构成的排列作为高层个体; Secondly, in order to search different regions in the solution space of DPFVTISP, ten kinds of low-layer heuristic operations, i.e., ten kinds of effective neighborhood operations, are designed, and their permutations are regarded as high-layer individuals.