Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
This provides a solution for camera calibration in challenging environments like dense crowds, but it is incremental as it builds on existing stereo and feature-based methods.
The paper tackles the problem of calibrating multiple cameras in homogeneous scenes without calibration objects by using stereo rigs with long and short focal length cameras, achieving accurate results validated in indoor and crowded outdoor settings.
In this paper we address the problem of multiple camera calibration in the presence of a homogeneous scene, and without the possibility of employing calibration object based methods. The proposed solution exploits salient features present in a larger field of view, but instead of employing active vision we replace the cameras with stereo rigs featuring a long focal analysis camera, as well as a short focal registration camera. Thus, we are able to propose an accurate solution which does not require intrinsic variation models as in the case of zooming cameras. Moreover, the availability of the two views simultaneously in each rig allows for pose re-estimation between rigs as often as necessary. The algorithm has been successfully validated in an indoor setting, as well as on a difficult scene featuring a highly dense pilgrim crowd in Makkah.