Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities
Research output: Journal contributions › Journal articles › Research › peer-review
Standard
In: World Electric Vehicle Journal, Vol. 15, No. 9, 382, 09.2024.
Research output: Journal contributions › Journal articles › Research › peer-review
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities
AU - Karim, Abdul
AU - Raza, Muhammad Amir
AU - Alharthi, Yahya Z.
AU - Abbas, Ghulam
AU - Othmen, Salwa
AU - Hossain, Md Shouquat
AU - Nahar, Afroza
AU - Mercorelli, Paolo
N1 - Publisher Copyright: © 2024 by the authors.
PY - 2024/9
Y1 - 2024/9
N2 - Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency.
AB - Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency.
KW - object detection
KW - object tracking
KW - sustainable transportation
KW - traffic incident
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85205228215&partnerID=8YFLogxK
U2 - 10.3390/wevj15090382
DO - 10.3390/wevj15090382
M3 - Journal articles
AN - SCOPUS:85205228215
VL - 15
JO - World Electric Vehicle Journal
JF - World Electric Vehicle Journal
SN - 2032-6653
IS - 9
M1 - 382
ER -