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Multi-target detection and positioning in crowds using multiple camera surveillance

机译：使用多摄像机监控在人群中进行多目标检测和定位

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In this study, we propose a pixel correspondence algorithm for positioning in crowds based on constraints on the distance between lines of sight, grayscale differences, and height in a world coordinates system. First, a Gaussian mixture model is used to obtain the background and foreground from multi-camera videos. Second, the hair and skin regions are extracted as regions of interest. Finally, the correspondences between each pixel in the region of interest are found under multiple constraints and the targets are positioned by pixel clustering. The algorithm can provide appropriate redundancy information for each target, which decreases the risk of losing targets due to a large viewing angle and wide baseline. To address the correspondence problem for multiple pixels, we construct a pixel-based correspondence model based on a similar permutation matrix, which converts the correspondence problem into a linear programming problem where a similar permutation matrix is found by minimizing an objective function. The correct pixel correspondences can be obtained by determining the optimal solution of this linear programming problem and the three-dimensional position of the targets can also be obtained by pixel clustering. Finally, we verified the algorithm with multiple cameras in experiments, which showed that the algorithm has high accuracy and robustness.
机译：在这项研究中，我们基于视线之间的距离，灰度差异和世界坐标系中的高度的约束条件，提出了一种像素对应算法，用于在人群中进行定位。首先，使用高斯混合模型从多摄像机视频中获取背景和前景。其次，将头发和皮肤区域提取为感兴趣区域。最后，在多个约束条件下找到感兴趣区域中每个像素之间的对应关系，并通过像素聚类对目标进行定位。该算法可以为每个目标提供适当的冗余信息，从而降低了由于大视角和宽基线而丢失目标的风险。为了解决多个像素的对应问题，我们基于相似的置换矩阵构造了基于像素的对应模型，该模型将对应问题转换为线性规划问题，在线性规划问题中，通过最小化目标函数找到了相似的置换矩阵。可以通过确定此线性编程问题的最佳解决方案来获得正确的像素对应关系，并且还可以通过像素聚类来获得目标的三维位置。最后，在实验中用多台摄像机对算法进行了验证，表明该算法具有较高的准确性和鲁棒性。

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