Enhancement of Accuracy Level in Parking Space Identification by using Machine Learning Algorithms

Title

Enhancement of Accuracy Level in Parking Space Identification by using Machine Learning Algorithms

Creator

Diniz A.M.; Samanta D.; Jayapandian N.

Description

Parking space identification is a crucial component in the development of intelligent transportation systems and smart cities. Accurate detection of parking spaces in urban areas can significantly improve traffic management, reduce congestion, and enhance overall parking efficiency. This proposed model is focuses on enhancing the accuracy of parking space identification through the utilization of Support Vector Machine (SVM) algorithms. The proposed methodology involves the following steps. First, a dataset comprising labelled parking space images is collected and pre-processed to ensure optimal quality and consistency. Next, feature extraction techniques are applied to capture certain relevant spatial and textural information from the images in the dataset, enabling the creation of informative feature vectors. These feature vectors are then utilized to train a SVM model, which is well-known for its capability to handle complex classification tasks. To measure the effectiveness of the SVM-based approach, a comprehensive set of experiments is carried out using real-world parking data. The performance metrics is to analysis accuracy level of the parking space identification. Comparative analysis has been done by comparing the proposed SVM approach with other popular machine learning algorithmsto demonstrate the superiority. The results indicate that the SVM-based model achieves a significantly higher accuracy level in parking space identification compared to other existing algorithms. 2023 IEEE.

Source

2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 595-600.

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Accuracy; Computer Vision; Convolutional Neural Networks; Deep Learning; Machine Learning; Support Vector Machine

Coverage

Diniz A.M., Department of CSE, CHRIST (Deemed to be University), India; Samanta D., Department of CSE, CHRIST (Deemed to be University), India; Jayapandian N., Department of CSE, CHRIST (Deemed to be University), India

Rights

Restricted Access

Relation

ISBN: 979-835034023-5

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICACRS58579.2023.10404454

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185373931&doi=10.1109%2fICACRS58579.2023.10404454&partnerID=40&md5=d6c76b583efaa5752d21a459696bc5fc