Real-time illegal parking detection algorithm in urban environments

Peng, X., Song, R., Cao, Q. , Li, Y., Cui, D., Jia, X., Lin, Z. and Huang, G.-B. (2022) Real-time illegal parking detection algorithm in urban environments. IEEE Transactions on Intelligent Transportation Systems, 23(11), pp. 20572-20587. (doi: 10.1109/TITS.2022.3180225)

Abstract

Currently, illegal parking detection tasks are mainly achieved through manually checking by enforcement officers on patrol or using Closed-Circuit Television (CCTV) cameras. However, these methods either need high human labour costs or demand installation costs and procedures. Therefore, illegal parking detection solutions, which can reduce significant labour and equipment installation costs, are highly demanded. This paper proposes a novel voting based detection algorithm using deep learning networks implemented using in-vehicle cameras to achieve illegal parking detection with multiple offences’ types. Adopting in-vehicle cameras better matches real-world mobile scenarios than using traditional CCTV cameras as this helps enforcement authorities to reduce manpower and installation costs. A well-constructed new dataset with more than 10 000 high-quality labelled images with seven object categories is built for illegal parking detection tasks. Additionally, one novel labelling method named “minimal illegal units” is proposed for illegal parking detection. It reduces the time and human labelling costs significantly, achieving a better correlation of a vehicle and its parking type. The experiments have been conducted in the urban areas of Singapore. Furthermore, the illumination robustness test has also been performed to illustrate that the proposed detection algorithm exhibits strong resistance to changing illumination conditions in varied operating environments. Our proposed detection algorithm can provide a benchmark for research in illegal parking detection.

• Item Type: Articles
• Additional Information: This work was supported by the National Research Foundation, Singapore through its AI Singapore Programme, under AI Singapore Award, under Grant AISG-100E-2018-014 and in part by National Research Foundation Singapore, Competitive Research Programme, under Grant NRF-CRP18-2017-02.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cao, Dr Qi
• Authors: Peng, X., Song, R., Cao, Q., Li, Y., Cui, D., Jia, X., Lin, Z., and Huang, G.-B.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Transactions on Intelligent Transportation Systems
• Publisher: IEEE
• ISSN: 1524-9050
• ISSN (Online): 1558-0016
• Published Online: 15 June 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Intelligent Transportation Systems 23(11): 20572-20587
• Publisher Policy: Reproduced in accordance with the publisher copyright policy