A machine learning technique for detecting outdoor parking

Abstract

In recent years, it has been observed that it becomes time-consuming and cumbersome job to find a vacant parking lot, especially in urban areas. Thus, it makes difficult for potential visitors or customers to search a vacant space for parking their vehicles and keeps on revolving round the parking area which not only increases frustration level but also wastes time and energy. In order to get an optimal parking lot immediately, there is a requirement of an efficient car-park routing systems. Current systems detecting vacant parking lots are either based on very expensive sensor based technology; or based on video based technologies which do not consider various weather conditions like sunny, cloudy and rainy weather. In the proposed work, a hybrid model is designed for detecting outdoor parking which detects the empty spaces available in the parking lots and the spaces/ slots getting vacant in the real-time scenario. This model is based on training, validating and testing the images (dataset) collected from various heights and angles of different parking areas stored in the repository. In this research, more advanced feature extractors and machine learning algorithms are evaluated in order to find the vacant parking lots in the outdoor park-ing areas.

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

“…The research considers both temporal and spatial patterns in parking slots. Another deep learning case study in Shoeibi and Shoeibi 23 and Mago and Kumar 24 using deep neural networks, a supervised learning technology, created an intelligent parking space that proposed hybrid robotic valets in a parking system that optimizes the use of parking spots.…”

Forecasting Parking Lots Availability: Analysis from a Real-World Deployment

“…al. [26] proposed another model for designing a parking management system based on video processing techniques. The proposed method detects available parking lots in real-time using cameras and assign vehicles entering the area to specific places.…”