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End-to-End Monocular Range Estimation for Forward Collision Warning
Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for objects from specifi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589406/ https://www.ncbi.nlm.nih.gov/pubmed/33096656 http://dx.doi.org/10.3390/s20205941 |
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author | Tang, Jie Li, Jian |
author_facet | Tang, Jie Li, Jian |
author_sort | Tang, Jie |
collection | PubMed |
description | Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for objects from specific categories relying on expensive object-level annotation for training, but not for unseen categories. In this paper, we present an end-to-end deep learning architecture to solve the above problems. Specifically, we represent the target range as a weighted sum of a set of potential distances. These potential distances are generated by inverse perspective projection based on intrinsic and extrinsic camera parameters, while a deep neural network predicts the corresponding weights of these distances. The whole architecture is optimized towards the range estimation task directly in an end-to-end manner with only the target range as supervision. As object category is not restricted in the training stage, the proposed method can generalize to objects with unseen categories. Furthermore, camera parameters are explicitly considered in the proposed method, making it able to generalize to images taken with different cameras and novel views. Additionally, the proposed method is not a pure black box, but provides partial interpretability by visualizing the produced weights to see which part of the image dominates the final result. We conduct experiments to verify the above properties of the proposed method on synthetic and real-world collected data. |
format | Online Article Text |
id | pubmed-7589406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75894062020-10-29 End-to-End Monocular Range Estimation for Forward Collision Warning Tang, Jie Li, Jian Sensors (Basel) Article Estimating range to the closest object in front is the core component of the forward collision warning (FCW) system. Previous monocular range estimation methods mostly involve two sequential steps of object detection and range estimation. As a result, they are only effective for objects from specific categories relying on expensive object-level annotation for training, but not for unseen categories. In this paper, we present an end-to-end deep learning architecture to solve the above problems. Specifically, we represent the target range as a weighted sum of a set of potential distances. These potential distances are generated by inverse perspective projection based on intrinsic and extrinsic camera parameters, while a deep neural network predicts the corresponding weights of these distances. The whole architecture is optimized towards the range estimation task directly in an end-to-end manner with only the target range as supervision. As object category is not restricted in the training stage, the proposed method can generalize to objects with unseen categories. Furthermore, camera parameters are explicitly considered in the proposed method, making it able to generalize to images taken with different cameras and novel views. Additionally, the proposed method is not a pure black box, but provides partial interpretability by visualizing the produced weights to see which part of the image dominates the final result. We conduct experiments to verify the above properties of the proposed method on synthetic and real-world collected data. MDPI 2020-10-21 /pmc/articles/PMC7589406/ /pubmed/33096656 http://dx.doi.org/10.3390/s20205941 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tang, Jie Li, Jian End-to-End Monocular Range Estimation for Forward Collision Warning |
title | End-to-End Monocular Range Estimation for Forward Collision Warning |
title_full | End-to-End Monocular Range Estimation for Forward Collision Warning |
title_fullStr | End-to-End Monocular Range Estimation for Forward Collision Warning |
title_full_unstemmed | End-to-End Monocular Range Estimation for Forward Collision Warning |
title_short | End-to-End Monocular Range Estimation for Forward Collision Warning |
title_sort | end-to-end monocular range estimation for forward collision warning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589406/ https://www.ncbi.nlm.nih.gov/pubmed/33096656 http://dx.doi.org/10.3390/s20205941 |
work_keys_str_mv | AT tangjie endtoendmonocularrangeestimationforforwardcollisionwarning AT lijian endtoendmonocularrangeestimationforforwardcollisionwarning |