Abstract In this paper an algorithm for multicamera pedestrian detection is proposed. The first stage of this work is based on the probabilistic occupancy map framework, in which the ground plane is discretized into a grid and the likelihood of pedestrian presence at each location is estimated by comparing a rectangle, of the average size of the pedestrians standing there, with the foreground silhouettes in all camera views. In the second stage, where we borrowed the idea from the Quine-McCluskey method for logic function minimization, essential candidates are initially identified, each of which covers at least a significant part of the foreground that is not covered by the other candidates. Then non-essential candidates are selected to cover the remaining foregrounds by following an iterative process, which alternates between merging redundant candidates and finding emerging essential candidates. Experiments on benchmark video datasets have demonstrated the improved performance of this algorithm in comparison with some benchmark non-deep or deep multicamera/monocular algorithms for pedestrian detection.

中文翻译：

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使用逻辑最小化的多摄像头行人检测

摘要 本文提出了一种多摄像头行人检测算法。这项工作的第一阶段是基于概率占用地图框架，其中地平面被离散化为一个网格，并通过比较一个矩形来估计每个位置的行人存在的可能性，该矩形与站在那里的行人的平均大小，在所有相机视图中都有前景剪影。在第二阶段，我们从 Quine-McCluskey 方法中借用了逻辑函数最小化的思想，初步确定了基本候选者，每个候选者至少覆盖了其他候选者未覆盖的前景的重要部分。然后通过迭代过程选择非必要的候选者来覆盖剩余的前景，它在合并冗余候选人和寻找新兴的重要候选人之间交替。在基准视频数据集上的实验表明，与一些用于行人检测的基准非深度或深度多摄像头/单目算法相比，该算法的性能有所提高。