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Extraction of image parking spaces in intelligent video surveillance systems

Bohush R., Yarashevich P., Ablameyko S., Kalganova T.

Machine Graphics and Vision

2018

Vol. 27, No. 1/4

47--62

This paper discusses the algorithmic framework for image parking lot localization and classiﬁcation for the video intelligent parking system. Perspective transformation, adaptive Otsu’s binarization, mathematical morphology operations, representation of horizontal lines as vectors, creating and ﬁltering vertical lines, and parking space coordinates determination are used for the localization of parking spaces in a video frame. The algorithm for classiﬁcation of parking spaces is based on the Histogram of Oriented Descriptors (HOG) and the Support Vector Machine (SVM) classiﬁer. Parking lot descriptors are extracted based on HOG. The overall algorithmic framework consists of the following steps: vertical and horizontal gradient calculation for the image of the parking lot, gradient module vector and orientation calculation, power gradient accumulation in accordance with cell orientations, blocking of cells, second norm calculations, and normalization of cell orientation in blocks. The parameters of the descriptor have been optimized experimentally. The results demonstrate the improved classiﬁcation accuracy over the class of similar algorithms and the proposed framework performs the best among the algorithms proposed earlier to solve the parking recognition problem.