Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections

TL;DR

This paper introduces a marker-free, online extrinsic camera calibration method that uses person keypoint detections from multiple views to achieve accurate, real-time calibration without traditional targets.

Contribution

The authors propose a novel online calibration approach relying solely on 2D human keypoints, eliminating the need for offline target-based calibration procedures.

Findings

Achieves lower reprojection errors than traditional offline calibration methods.

Converges to accurate camera poses within a few minutes of scene observation.

Operates in real-world settings with only local keypoint detections.

Abstract

Calibration of multi-camera systems, i.e. determining the relative poses between the cameras, is a prerequisite for many tasks in computer vision and robotics. Camera calibration is typically achieved using offline methods that use checkerboard calibration targets. These methods, however, often are cumbersome and lengthy, considering that a new calibration is required each time any camera pose changes. In this work, we propose a novel, marker-free online method for the extrinsic calibration of multiple smart edge sensors, relying solely on 2D human keypoint detections that are computed locally on the sensor boards from RGB camera images. Our method assumes the intrinsic camera parameters to be known and requires priming with a rough initial estimate of the camera poses. The person keypoint detections from multiple views are received at a central backend where they are synchronized, filtered, and assigned to person hypotheses. We use these person hypotheses to repeatedly solve optimization problems in the form of factor graphs. Given suitable observations of one or multiple persons traversing the scene, the estimated camera poses converge towards a coherent extrinsic calibration within a few minutes. We evaluate our approach in real-world settings and show that the calibration with our method achieves lower reprojection errors compared to a reference calibration generated by an offline method using a traditional calibration target.