Person detection methods are used widely in applications including visual surveillance, pedestrian detection, and robotics. However, accurate detection of persons from overhead fisheye images remains an open challenge because of factors including person rotation and small-sized persons. To address the person rotation problem, we convert the fisheye images into panoramic images. For smaller people, we focused on the geometry of the panoramas. Conventional detection methods tend to focus on larger people because these larger people yield large significant areas for feature maps. In equirectangular panoramic images, we find that a person's height decreases linearly near the top of the images. Using this finding, we leverage the significance values and aggregate tokens that are sorted based on these values to balance the significant areas. In this leveraging process, we introduce panoramic distortion-aware tokenization. This tokenization procedure divides a panoramic image using self-similarity figures that enable determination of optimal divisions without gaps, and we leverage the maximum significant values in each tile of token groups to preserve the significant areas of smaller people. To achieve higher detection accuracy, we propose a person detection and localization method that combines panoramic-image remapping and the tokenization procedure. Extensive experiments demonstrated that our method outperforms conventional methods when applied to large-scale datasets.

翻译：人员检测方法广泛应用于视觉监控、行人检测和机器人技术等领域。然而，由于人员旋转和小尺寸人员等因素，从俯视鱼眼图像中准确检测人员仍是一个开放挑战。为解决人员旋转问题，我们将鱼眼图像转换为全景图像。针对较小尺寸人员，我们聚焦于全景图像的几何特性。传统检测方法往往更关注较大尺寸人员，因为这些人员能在特征图中产生显著区域。在等距柱状全景图像中，我们发现人员高度在图像顶部附近呈线性递减。基于这一发现，我们利用显著性值并聚合根据这些值排序的token，以平衡显著区域。在此过程中，我们引入了全景畸变感知分词方法。该分词流程通过自相似性图划分全景图像，实现无间隙的最优分割，并利用token组中每个分块的最大显著性值来保留较小尺寸人员的显著区域。为实现更高的检测精度，我们提出了一种结合全景图像重映射与分词流程的人员检测与定位方法。大量实验证明，该方法在大规模数据集上优于传统方法。