Vacant parking‐slot recognition is one of the core elements for achieving a fully automated parking assistant system. The recognition of a vacant parking‐slot is typically executed in two stages: parking‐slot recognition and slot‐occupancy classification. In most previous works, considering the recognised slot results as a grid cell, the existence of objects is evaluated through the slot examination in slot‐occupancy classification. Despite moderate performance, however, these methods cannot distinguish the parking‐slot availability of moving or small objects and permit marks, since all previous methods only focus on the occupancy of typically parked vehicles. In this study, the authors present a camera‐based available parking‐slot recognition algorithm based on slot‐context analysis. By utilising abundant visual features, they propose a new algorithm handling diverse available parking‐slot conditions. For parking‐slot recognition, they propose a new methodology of extracting and associating line‐markings. The proposed algorithm includes a slot‐validation step that identifies multiple slot contexts in a probabilistic integration and has more flexibility on irregular patterns. In the slot‐occupancy classification stage, reliable parking‐availability detection is achieved through visual‐slot features including the histogram of gradient and frequency–magnitude features, via a support vector machine. Simulations and vehicle‐level experiments demonstrate the robustness of the proposed algorithm in diverse conditions.
Available parking slot recognition based on slot context analysis
IET Intelligent Transport Systems ; 10 , 9 ; 594-604
2016-11-01
11 pages
Article (Journal)
Electronic Resource
English
cameras , line‐marking association , object detection , small objects , vacant‐parking‐slot recognition , slot‐validation , probabilistic integration , line‐marking extraction , image classification , histogram of gradient , camera‐based available parking‐slot recognition algorithm , visual‐slot features , parking‐availability detection , diverse‐available parking‐slot condition handling , slot‐occupancy classification , probability , visual features , road traffic control , support vector machine , feature extraction , support vector machines , moving objects , fully‐automated parking assistant system , frequency–magnitude features , irregular pattern flexibility , slot context analysis
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