Abstract We introduce and evaluate a novel camera pose estimation framework that uses the human head as calibration object. The proposed method facilitates extrinsic from 2D input images (NIR and/or RGB), while merely relying on detected head, without need for depth information. approach is applicable to single cameras or multi-camera networks. Our implementation fine-tuned deep learning-based ...

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A method is introduced to track the object’s motion and estimate its pose from multiple cameras. We focus on direct estimation of the 3D pose from 2D image sequences. We derive a distributed solution that is equivalent to the centralized pose estimation from multiple cameras. Moreover, we show that, by using a proper rotation between each camera and a fixed camera view, we can rely on independe...