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Symmetry 2018, 10(3), 64; https://doi.org/10.3390/sym10030064

Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach

School of Software Engineering, Tongji University, Shanghai 201804, China
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Received: 26 February 2018 / Revised: 7 March 2018 / Accepted: 12 March 2018 / Published: 13 March 2018
(This article belongs to the Special Issue Advanced in Artificial Intelligence and Cloud Computing)
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Abstract

Recent years have witnessed a growing interest in developing automatic parking systems in the field of intelligent vehicles. However, how to effectively and efficiently locating parking-slots using a vision-based system is still an unresolved issue. Even more seriously, there is no publicly available labeled benchmark dataset for tuning and testing parking-slot detection algorithms. In this paper, we attempt to fill the above-mentioned research gaps to some extent and our contributions are twofold. Firstly, to facilitate the study of vision-based parking-slot detection, a large-scale parking-slot image database is established. This database comprises 8600 surround-view images collected from typical indoor and outdoor parking sites. For each image in this database, the marking-points and parking-slots are carefully labeled. Such a database can serve as a benchmark to design and validate parking-slot detection algorithms. Secondly, a learning-based parking-slot detection approach, namely P S D L , is proposed. Using P S D L , given a surround-view image, the marking-points will be detected first and then the valid parking-slots can be inferred. The efficacy and efficiency of P S D L have been corroborated on our database. It is expected that P S D L can serve as a baseline when the other researchers develop more sophisticated methods. View Full-Text
Keywords: parking assistance systems; parking-slot detection; AdaBoost; decision tree parking assistance systems; parking-slot detection; AdaBoost; decision tree
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Zhang, L.; Li, X.; Huang, J.; Shen, Y.; Wang, D. Vision-Based Parking-Slot Detection: A Benchmark and A Learning-Based Approach. Symmetry 2018, 10, 64.

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