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Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System

Authors :
Byeongjoon Noh
Hansaem Park
Sungju Lee
Seung-Hee Nam
Source :
Sensors, Vol 22, Iss 9, p 3451 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users’ risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.

Details

Language :
English
ISSN :
22093451 and 14248220
Volume :
22
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.6ea3aa88fcb74155aae6cda9d8accdb5
Document Type :
article
Full Text :
https://doi.org/10.3390/s22093451