Fast automatic camera network calibration through human mesh recovery

Abstract

Camera calibration is a necessary preliminary step in computer vision for the estimation of the position of objects in the 3D world. Despite the intrinsic camera parameters can be easily computed offline, extrinsic parameters need to be computed each time a camera changes its position, thus not allowing for fast and dynamic network re-configuration. In this paper we present an unsupervised and automatic framework for the estimation of the extrinsic parameters of a camera network, which leverages on optimised 3D human mesh recovery from a single image, and which does not require the use of additional markers. We show how it is possible to retrieve the real-world position of the cameras in the network together with the floor plane, exploiting regular RGB images and with a weak prior knowledge of the internal parameters. Our framework can also work with a single camera and in real-time, allowing the user to add, re-position, or remove cameras from the network in a dynamic fashion.

Robust Extrinsic Calibration of Multiple RGB-D Cameras with Body Tracking and Feature Matching

“…Thus, it is possible to transform a camera coordinate system into each joint coordinate system. Similarly, Garau et al [ 14 ] proposed an unsupervised automatic framework for calibration outside the camera. It uses an optimized 3D human mesh recovery from a single image.…”

Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras

“…[15] realizes camera calibration by using the depth camera in an indoor scene to extract the skeleton. [49,20,54] and [21] use detected human 2D joints and mesh respectively to calibrate the camera, further simplifying the calibration device. State-of-the-art 2D/3D pose estimation frameworks [18,7,32] can hardly get accurate 2D/3D keypoints in multi-person scenes, and such methods cannot be directly applied to multi-person cases.…”