Detection and 3D reconstruction of traffic signs from multiple view color images

Soheilian, Bahman; Paparoditis, Nicolas; Vallet, Bruno

doi:10.1016/j.isprsjprs.2012.11.009

Cited by 63 publications

(53 citation statements)

References 34 publications

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“…Namely by studying its behavior facing more noises on initial 2D points (used for ellipse fitting) and camera rotation matrix but also with more images in complicated camera configurations. Then we aim at evaluating its performance on real data in reconstruction of circular traffic signs (in the same framework developed for reconstruction of polygonal road signs (Soheilian et al, 2013)) and also in detection and reconstruction of circular photogrammetric calibration targets.…”

Section: Discussionmentioning

confidence: 99%

Multi-view 3D circular target reconstruction with uncertainty analysis

Soheilian¹,

Brédif²

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The paper presents an algorithm for reconstruction of 3D circle from its apparition in n images. It supposes that camera poses are known up to an uncertainty. They will be considered as observations and will be refined during the reconstruction process. First, circle apparitions will be estimated in every individual image from a set of 2D points using a constrained optimization. Uncertainty of 2D points are propagated in 2D ellipse estimation and leads to covariance matrix of ellipse parameters. In 3D reconstruction process ellipse and camera pose parameters are considered as observations with known covariances. A minimal parametrization of 3D circle enables to model the projection of circle in image without any constraint. The reconstruction is performed by minimizing the length of observation residuals vector in a non linear Gauss-Helmert model. The output consists in parameters of the corresponding circle in 3D and their covariances. The results are presented on simulated data.

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Section: Discussionmentioning

confidence: 99%

Multi-view 3D circular target reconstruction with uncertainty analysis

Soheilian¹,

Brédif²

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Several methods have been developed to map particular types of objects from street-level imagery: manholes [12], telecom assets [10], road signs [29], and traffic lights [30,31]. These methods rely on position triangulation from individual camera views to geolocate the considered road furniture elements.…”

Section: Related Workmentioning

confidence: 99%

Automatic Discovery and Geotagging of Objects from Street View Imagery

2018

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Abstract:Many applications, such as autonomous navigation, urban planning, and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper, we propose the automatic detection and computation of the coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images, while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov random field model to estimate the objects' geolocation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with the simultaneous presence of multiple, visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and position precision of approximately 2 m, which is approaching the precision of single-frequency GPS receivers.

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“…A breakthrough in the process of 3D image generation was made in 1976 when Kreiling developed a method of generating epipolar images [3,9,27,33]. However, it is possible to project stereogram images onto the common plane if the camera constants and the elements of relative orientation are known.…”

Section: Introductionmentioning

confidence: 99%

Internet Photogrammetry for Inspection of Seaports

Paszotta

Szumiło

Szulwic

2017

Polish Maritime Research

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This paper intends to point out the possibility of using Internet photogrammetry to construct 3D models from the images obtained by means of UAVs (Unmanned Aerial Vehicles In this paper the authors recommend a solution useful for creating 3D models from images acquired by the UAV using non-metric images from digital cameras. The developed algorithms, created and presented software allows to generate 3D models through the Internet in two modes: anaglyph and display in shutter systems. The problem of 3D image generation in photogrammetry is solved by using epipolar images. The appropriate method was presented by Kreiling in 1976. However, it applies to photogrammetric images for which the internal orientation is known. In the case of digital images obtained with non-metric cameras it is required to use another solution based on the fundamental matrix concept, introduced by Luong in 1992. In order to determine the matrix which defines the relationship between left and right digital image it is required to have at least eight homologous points. To determine the solution it is necessary to use the SVD (singular value decomposition). By using the fundamental matrix the epipolar lines are determined, which makes the correct orientation of images making stereo pairs, possible. The appropriate mathematical bases and illustrations are included in the publication.

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Detection and 3D reconstruction of traffic signs from multiple view color images

Cited by 63 publications

References 34 publications

Multi-view 3D circular target reconstruction with uncertainty analysis

Multi-view 3D circular target reconstruction with uncertainty analysis

Automatic Discovery and Geotagging of Objects from Street View Imagery

Internet Photogrammetry for Inspection of Seaports

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