Automatic Vision-Based Parking Slot Detection and Occupancy Classification

TL;DR

This paper introduces an automatic vision-based system for detecting parking slots and classifying their occupancy status using image analysis and deep learning, eliminating manual labeling and improving robustness.

Contribution

It presents a novel algorithm that detects parking slots automatically from images and classifies occupancy with a trained deep classifier, enhancing accuracy and robustness.

Findings

High detection accuracy on public datasets

Robustness to occlusions and illegal parking

Effective occupancy classification with deep learning

Abstract

Parking guidance information (PGI) systems are used to provide information to drivers about the nearest parking lots and the number of vacant parking slots. Recently, vision-based solutions started to appear as a cost-effective alternative to standard PGI systems based on hardware sensors mounted on each parking slot. Vision-based systems provide information about parking occupancy based on images taken by a camera that is recording a parking lot. However, such systems are challenging to develop due to various possible viewpoints, weather conditions, and object occlusions. Most notably, they require manual labeling of parking slot locations in the input image which is sensitive to camera angle change, replacement, or maintenance. In this paper, the algorithm that performs Automatic Parking Slot Detection and Occupancy Classification (APSD-OC) solely on input images is proposed. Automatic parking slot detection is based on vehicle detections in a series of parking lot images upon which clustering is applied in bird's eye view to detect parking slots. Once the parking slots positions are determined in the input image, each detected parking slot is classified as occupied or vacant using a specifically trained ResNet34 deep classifier. The proposed approach is extensively evaluated on well-known publicly available datasets (PKLot and CNRPark+EXT), showing high efficiency in parking slot detection and robustness to the presence of illegal parking or passing vehicles. Trained classifier achieves high accuracy in parking slot occupancy classification.