Related papers: Combining geolocation and height estimation of objects from street level imagery

Combining geolocation and height estimation of objects from street level imagery

URL: http://arxiv.org/abs/2305.08232v1
Date: Sun, 14 May 2023 19:40:02 GMT
Title: Combining geolocation and height estimation of objects from street level imagery
Authors: Matej Ulicny, Vladimir A. Krylov, Julie Connelly, and Rozenn Dahyot
Abstract summary: We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery. The proposed technique uses image metadata along with coordinates of objects detected in the image plane as found by a custom-trained Convolutional Neural Network.
Score: 5.887281983256354
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery, which is considered as a single available input data modality. Our solution is formulated via Markov Random Field optimization with deterministic output. The proposed technique uses image metadata along with coordinates of objects detected in the image plane as found by a custom-trained Convolutional Neural Network. Computing the object height using our methodology, in addition to object geolocation, has negligible effect on the overall computational cost. Accuracy is demonstrated experimentally for water drains and road signs on which we achieve average elevation estimation error lower than 20cm.

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