Multi-view traffic sign detection, recognition, and 3D localisation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00303545" target="_blank" >RIV/68407700:21230/14:00303545 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s00138-011-0391-3" target="_blank" >https://doi.org/10.1007/s00138-011-0391-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00138-011-0391-3" target="_blank" >10.1007/s00138-011-0391-3</a>

Result language

angličtina

Original language name

Multi-view traffic sign detection, recognition, and 3D localisation

Original language description

Several applications require information about street furniture. Part of the task is to survey all traffic signs. This has to be done for millions of km of road, and the exercise needs to be repeated every so often. We used a van with eight roof-mounted cameras to drive through the streets and took images every meter. The paper proposes a pipeline for the efficient detection and recognition of traffic signs from such images. The task is challenging, as illumination conditions change regularly, occlusions are frequent, sign positions and orientations vary substantially, and the actual signs are far less similar among equal types than one might expect. We combine 2D and 3D techniques to improve results beyond the state-of-the-art, which is still very much preoccupied with single view analysis. For the initial detection in single frames, we use a set of colour- and shape-based criteria. They yield a set of candidate sign patterns. The selection of such candidates allows for a significant speed up over a sliding window approach while keeping similar performance. A speedup is also achieved through a proposed efficient bounded evaluation of AdaBoost detectors. The 2D detections in multiple views are subsequently combined to generate 3D hypotheses. A Minimum Description Length formulation yields the set of 3D traffic signs that best explains the 2D detections. The paper comes with a publicly available database, with more than 13,000 traffic signs annotations.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2014

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Machine Vision and Applications

ISSN

0932-8092

e-ISSN

1432-1769

Volume of the periodical

25

Issue of the periodical within the volume

3

Country of publishing house

DE - GERMANY

Number of pages

15

Pages from-to

633-647

UT code for WoS article

000333364300007

EID of the result in the Scopus database

2-s2.0-84899122880