Optimized outdoor parking system for smart cities using advanced saliency detection method and hybrid features extraction model

Abstract

As a new concept in urban development, smart cities are characterized primarily by their mobility. To solve these problems, it became necessary to develop an intelligent system. Using the Advanced Saliency Detection Method and an Efficient Features Extraction Model, the proposed work is aimed at detecting vacant outdoor parking lots. Experimental work has been conducted using the publicly available “PKLot” dataset, which consists of 695,899 segmented images. Under different weather conditions, the images were taken from three different camera locations in two different parking lots in Brazil, including sunny, cloudy, and rainy days. The experimental results mentioned that the hybrid feature extraction model enhanced the performance of parking detection systems. Using three different datasets, PUCPR, UFPR04, and UFPR05, we obtain an accuracy of 99.93%, 99.89%, and 99.87%. This is clear that the hybrid feature extraction model with the PUCPR dataset has produced the highest accuracy.

KEYWORDS:

• Smart cities
• parking system
• pre-processing
• feature extraction
• hybrid feature extraction model
• classification

Disclosure statement

No potential conflict of interest was reported by the author(s).