Available parking slot recognition based on slot context analysis

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.