Conference proceeding
A Deep Learning based Illegal Parking Detection Platform
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on ai for geographic knowledge discovery, pp.32-35
GeoAI 2019
11/05/2019
DOI: 10.1145/3356471.3365233
Abstract
Illegal parking is a critical problem in large, growing cities. Currently, the responsibility for detecting illegally parked vehicles has been left to law enforcement, which often requires manual inspection. To improve the efficiency of law enforcement for vehicle parking management, we propose a web-based analytic platform that leverages recent advancements in computer vision. This proposed platform provides an algorithm to improve the performance of detecting vehicle license plates from videos, based on an existing deep learning approach. Also, we provide a method to estimate vehicle parking locations. This platform is applicable for videos of security patrolling. End-users can define restricted zones via a map-based interface and all vehicles located in these areas can be efficiently identified once patrolling videos are received. This system is evaluated by two videos captured in real-world parking lots. The results indicate that the proposed platform can successfully identify vehicle plate numbers and estimate their parking locations to support the management of urban parking infrastructure.
Details
- Title: Subtitle
- A Deep Learning based Illegal Parking Detection Platform
- Creators
- Zhengcong Yin - Texas A&M UniversityHaoyi Xiong - University of IowaXun Zhou - University of IowaDaniel Goldberg - Texas A&M UniversityDave Bennett - University of IowaChong Zhang - Environmental Systems Research Institute (United States)
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 3rd ACM SIGSPATIAL International Workshop on ai for geographic knowledge discovery, pp.32-35
- Series
- GeoAI 2019
- DOI
- 10.1145/3356471.3365233
- Publisher
- ACM
- Language
- English
- Date published
- 11/05/2019
- Academic Unit
- Business Analytics; Geographical and Sustainability Sciences
- Record Identifier
- 9984259633102771
Metrics
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