Abstract
Designing robust activity detectors for fixed camera surveillance video requires knowledge of the 3-D scene. This paper presents an automatic camera calibration process that provides a mechanism to reason about the spatial proximity between objects at different times. It combines a CNN-based camera pose estimator with a vertical scale provided by pedestrian observations to establish the 4-D scene geometry. Unlike some previous methods, the people do not need to be tracked nor do the head and feet need to be explicitly detected. It is robust to individual height variations and camera parameter estimation errors.
Abstract (translated)
为固定摄像机监控视频设计强大的活动探测器需要了解三维场景。本文介绍了一种自动摄像机标定过程，为不同时间物体之间空间接近的原因提供了一种机制。它结合了一个基于CNN的摄像机姿态估计和行人观测提供的垂直尺度，以建立4-D场景几何。与以前的一些方法不同，人们不需要被跟踪，也不需要显式地检测头和脚。它对个别高度变化和相机参数估计误差具有鲁棒性。
URL
https://arxiv.org/abs/1906.01675
