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arXiv · 2212.09298

From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration

Zekun Qian·Ruize Han·Wei Feng·Feifan Wang·Song Wang
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Abstract

We tackle a new problem of multi-view camera and subject registration in the bird's eye view (BEV) without pre-given camera calibration. This is a very challenging problem since its only input is several RGB images from different first-person views (FPVs) for a multi-person scene, without the BEV image and the calibration of the FPVs, while the output is a unified plane with the localization and orientation of both the subjects and cameras in a BEV. We propose an end-to-end framework solving this problem, whose main idea can be divided into following parts: i) creating a view-transform subject detection module to transform the FPV to a virtual BEV including localization and orientation of each pedestrian, ii) deriving a geometric transformation based method to estimate camera localization and view direction, i.e., the camera registration in a unified BEV, iii) making use of spatial and appearance information to aggregate the subjects into the unified BEV. We collect a new large-scale synthetic dataset with rich annotations for evaluation. The experimental results show the remarkable effectiveness of our proposed method.

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Zekun Qian, Ruize Han, Wei Feng, Feifan Wang, Song Wang. 2022-12-19. From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration. https://arxiv.org/abs/2212.09298

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