ParkSense: A Computer Vision-Driven Parking Assistant

Authors

  • Eraf Ali Undergraduate Student, Department of Electrical Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
  • Osama Zaheer Undergraduate Student, Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
  • Mohd Altamash Undergraduate Student, Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
  • Zuhair Arif Undergraduate Student, Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
  • Abdullah Awais Undergraduate Student, Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
  • Ash Mohammad Abbas Professor, Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
  • Shah Ahmed Shakir Abu Asim Khan Postgraduate Student, Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
  • Mohammad Rayyan Postgraduate Student, Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
  • Ahmad Bilal Zaidi Postgraduate Student, Department of Computer Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

DOI:

https://doi.org/10.46610/JIBDSN.2025.v02i03.002

Keywords:

Android application, Automatic parking, Computer vision, OpenCV, PKLot dataset, SVM, Smart systems

Abstract

The growth of urban populations has led to increasing demand for efficient parking management systems. Traditional manual parking mechanisms often result in wasted time, traffic congestion, and inefficient space utilization. This paper presents the design and development of an Automatic Car Parking Mobile Application that integrates computer vision, machine learning, and cloud-based monitoring to optimize parking operations. Using real-time image processing via OpenCV and a Support Vector Machine (SVM) classifier trained on the PKLot dataset, the system detects parking space availability and updates users through an Android interface. The app offers secure login, location-based slot selection, and live occupancy detection, improving both user convenience and parking management efficiency.

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Published

2025-11-27

Issue

Vol. 2 No. 3 (2025): September –December

Section

Articles