Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections

• URL: http://arxiv.org/abs/2209.07393v1
• Date: Thu, 15 Sep 2022 15:54:21 GMT
• Title: Online Marker-free Extrinsic Camera Calibration using Person Keypoint Detections
• Authors: Bastian P\"atzold, Simon Bultmann, Sven Behnke
• Abstract summary: We propose a marker-free online method for the extrinsic calibration of multiple smart edge sensors. Our method assumes the intrinsic camera parameters to be known and requires priming with a rough initial estimate of the camera poses. We show that the calibration with our method achieves lower reprojection errors compared to a reference calibration generated by an offline method.
• Score: 25.393382192511716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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