Abstract
Unobtrusive monitoring of distances between people indoors is a useful tool in the fight against pandemics. A natural resource to accomplish this are surveillance cameras. Unlike previous distance estimation methods, we use a single, overhead, fisheye camera with wide area coverage and propose two approaches. One method leverages a geometric model of the fisheye lens, whereas the other method uses a neural network to predict the 3D-world distance from people-locations in a fisheye image. To evaluate our algorithms, we collected a first-of-its-kind dataset using single fisheye camera, that comprises a wide range of distances between people (1-58 ft) and will be made publicly available. The algorithms achieve 1-2 ft distance error and over 95% accuracy in detecting social-distance violations.
Abstract (translated)
室内人员间的距离监测是对抗疫情的一种有用工具。实现这一目标的自然资源是监控摄像头。与以前的距离估计方法不同,我们使用一种具有广泛区域覆盖范围的 overhead 大视野显微镜,并提出两种方法。一种方法利用显微镜的几何模型,而另一种方法使用神经网络预测从显微镜图像中的人地点到三维世界的距離。为了评估我们的算法,我们使用单个显微镜相机收集了一个独特的数据集,该数据集包括人们之间的广泛距离范围(1-58英尺),并将公开发布。算法在检测社交距离违反行为方面实现了1-2英尺的距离误差,并超过95%的准确率。
URL
https://arxiv.org/abs/2303.11520
