A coarse-to-fine framework for multiple pedestrian crossing detection
|Author:||Fan, Yuhua1,2; Sun, Zhonggui1; Zhao, Guoying2|
1School of Mathematical Science, Liaocheng University, Liaocheng 252000, China
2Center for Machine Vision and Signal Analysis, University of Oulu, University of Oulu, 90570 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 5.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020092575803
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2020-09-25
When providing route guidance to pedestrians, one of the major safety considerations is to ensure that streets are crossed at places with pedestrian crossings. As a result, map service providers are keen to gather the location information about pedestrian crossings in the road network. Most, if not all, literature in this field focuses on detecting the pedestrian crossing immediately in front of the camera, while leaving the other pedestrian crossings in the same image undetected. This causes an under-utilization of the information in the video images, because not all pedestrian crossings captured by the camera are detected. In this research, we propose a coarse-to-fine framework to detect pedestrian crossings from probe vehicle videos, which can then be combined with the GPS traces of the corresponding vehicles to determine the exact locations of pedestrian crossings. At the coarse stage of our approach, we identify vanishing points and straight lines associated with the stripes of pedestrian crossings, and partition the edges to obtain rough candidate regions of interest (ROIs). At the fine stage, we determine whether these candidate ROIs are indeed pedestrian crossings by exploring their prior constraint information. Field experiments in Beijing and Shanghai cities show that the proposed approach can produce satisfactory results under a wide variety of situations.
|Pages:||1 - 16|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
Yuhua Fan was funded by the PhD Research startup Foundation of Liaocheng University (No.318051654) and a project of Shandong Province Higher Educational Science and Technology Program (No.KJ2018BAN109). Guoying Zhao was supported by the Academy of Finland for project MiGA (grant 316765), ICT 2023 project (grant 328115), and Infotech Oulu.
|Academy of Finland Grant Number:||
316765 (Academy of Finland Funding decision)
328115 (Academy of Finland Funding decision)
© 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/).