Current Result Document :
| 한글제목(Korean Title) |
딥 러닝 기반 객체 감지 및 추적을 통한 차량 트래픽 관리 시스템 |
| 영문제목(English Title) |
Vehicular traffic management with Deep Learning driven object detection and tracking |
| 저자(Author) |
Sardar Jaffar Ali
양희규
Syed M. Raza
김문성
추현승
Huigyu Yang
Moonseong Kim
Hyunseung Choo
|
| 원문수록처(Citation) |
VOL 23 NO. 02 PP. 0077 ~ 0078 (2022. 10) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Due to the increase in vehicular traffic, smart technologies must be deployed that cannot only store data but also use it to improve traffic performance and optimization. Different models for detection and tracking have been presented in the literature. Although there are a few applications available for traffic monitoring, there still is a scope of improvement in areas where there is high traffic density due to limited space or where aerial monitoring is required. In this study,, we presented a research project which aims to implement pre-trained YOLO model for CCTV dataset. Moreover, we have re-trained YOLOv4 for drones using aerial image dataset and estimated the accuracy of the proposed models. In the end, we have critically analyzed our work and provided future research directions which will boost the performance of this work. |
| 키워드(Keyword) |
|
| 파일첨부 |
PDF 다운로드
|
