Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System
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 additi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/9/3451 |
| _version_ | 1797502782148706304 |
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| author | Byeongjoon Noh Hansaem Park Sungju Lee Seung-Hee Nam |
| author_facet | Byeongjoon Noh Hansaem Park Sungju Lee Seung-Hee Nam |
| author_sort | Byeongjoon Noh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:41:01Z |
| format | Article |
| id | doaj.art-6ea3aa88fcb74155aae6cda9d8accdb5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:41:01Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6ea3aa88fcb74155aae6cda9d8accdb52023-11-23T09:18:39ZengMDPI AGSensors1424-82202022-04-01229345110.3390/s22093451Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety SystemByeongjoon Noh0Hansaem Park1Sungju Lee2Seung-Hee Nam3Applied Science Research Institute, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseung-gu, Daejeon 34141, KoreaDepartment of Civil and Environmental Engineering, Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseung-gu, Daejeon 34141, KoreaDepartment of Software, Sangmyung University, Cheonan 31066, KoreaCenter for Accelerator Research, Korea University, Sejong 30019, KoreaCrosswalks 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.https://www.mdpi.com/1424-8220/22/9/3451crossing behavior analysispedestrian safetypotential collision riskscomputer vision |
| spellingShingle | Byeongjoon Noh Hansaem Park Sungju Lee Seung-Hee Nam Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System Sensors crossing behavior analysis pedestrian safety potential collision risks computer vision |
| title | Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System |
| title_full | Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System |
| title_fullStr | Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System |
| title_full_unstemmed | Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System |
| title_short | Vision-Based Pedestrian’s Crossing Risky Behavior Extraction and Analysis for Intelligent Mobility Safety System |
| title_sort | vision based pedestrian s crossing risky behavior extraction and analysis for intelligent mobility safety system |
| topic | crossing behavior analysis pedestrian safety potential collision risks computer vision |
| url | https://www.mdpi.com/1424-8220/22/9/3451 |
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