Curbside Parking Occupancy Detection - Dashcam-Based Solutions

被引:1
作者
Li, Jiayu [1 ]
Zhang, Hanming [1 ]
Hu, Juhua [1 ]
Cheng, Wei [1 ]
机构
[1] Univ Washington, Tacoma Sch Engn & Technol, Seattle, WA 98195 USA
来源
PROCEEDINGS OF THE 2024 25TH IEEE INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT, MDM 2024 | 2024年
关键词
D O I
10.1109/MDM61037.2024.00046
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Accurate assessment of curbside parking occupancy is essential for policymakers to optimize public resource allocation. It is also beneficial for driver/autonomous vehicles' parking planning. Despite its importance, there is still no comprehensive solution of curbside parked vehicle detection for different road and traffic scenarios. We therefore propose two computer vision based solutions for efficiently quantify parked vehicles in simple and complex scenarios, respectively, from the street videos taken by off-the -shelf dash cameras. The proposed Al pipelines encompass multiple tasks, including vehicle detection and tracking, road surface detection, and lane line detection. The interplay between detected vehicles, road surface, and lane lines enhances the robustness of feature engineering. Through evaluations, our solutions demonstrate their capability to handle diverse road and traffic scenarios, including busy main roads, quiet side roads, and residential areas.
引用
收藏
页码:219 / 226
页数:8
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