Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System
AI-ready brief
: Crosswalks present a major threat to pedestri ans, but we lack dense behavioral data to investigate the risks they face. One of the breakthr oughs 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 infrastructure s.
Author abstract
: Crosswalks present a major threat to pedestri ans, but we lack dense behavioral data to investigate the risks they face. One of the breakthr oughs 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 infrastructure s. In order to capture these subt le 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 obta in 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 proces s from partitioning video to extr acting 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 fe asibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.
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Citation-ready BibTeX
@article{noh2022visionbasedpedestrianscr,
title = {Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System},
author = {B. Noh and H. Park and S. Lee and S. Nam},
year = {2022},
journal = {Sensors , 22(9), 2022.}
}