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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 01 | Jan 2024

p-ISSN: 2395-0072

www.irjet.net

Affordable mobile application camera system to monitor residential societies vehicle activity AVULA MAHA LAKSHMI1, AABIROO ARSHAD2, CHEJERALA SOWBHAGYA DEEPIKA 3 Assistant prof SHEIK JAMIL AHMED 4 1,2,3 Student, B. Tech (Computer Science and Engineering), Presidency University, Bangalore, India. 4Assistant Professor, Computer Science and Engineering Department, Presidency University, Bangalore, India. ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - This project presents a machine learning

predictive analytics for traffic low and safety measures. Additionally, the system will be equipped with an of line mode to ensure accessibility in remote locations.

identify and authorize entry while keeping records of enabled surveillance system designed for real-time monitoring and tracking of vehicles in urban and residential environments. With rapid advancements in autonomous technologies, there is an increasing demand for intelligent systems that can enhance public safety, streamline traffic management, and provide secure access control within private and public spaces. The proposed system leverages deep learning algorithms for vehicle detection, classification, and speed monitoring, while utilizing IoT infrastructure to enable seamless data collection and remote access.

II. LITERATURE REVIEW ML Enabled Surveillance System for Societies [1]: Recent studies emphasis the role of IoT and machine learning in improving vehicle monitoring systems. These technologies enable real-time vehicle tracking and traffic violation detection. Traditional systems like speed guns are costly and limited to single-lane monitoring. In contrast, IoTbased solutions are more scalable, cost-effective, and automated. Mobile apps enhance security by providing realtime alerts and data access for both residents and security personnel. However, the affordability of these solutions is a major consideration for broader implementation.

Key Words: Machine Learning, IoT, Surveillance System, Vehicle Tracking, Autonomous Technology, Deep Learning, Real-time Monitoring, Smart City, Traffic Management, License Plate Recognition, Predictive Analysis, Access Control, Situational Awareness.

jimmy_21Vehicle Tracking System [2]: The proposed Vehicle Tracking System integrates GPS, GSM, and web-based technologies to offer a comprehensive solution for vehicle owners, facilitating real-time tracking and enhanced management capabilities. It utilizes an advanced GY-NEO6MV2 GPS module, allowing for location accuracy within 10 meters, which is consistent with existing systems reviewed in the literature. The system architecture includes an IoT platform based on Arduino, along with a GSM module for communication. The web application is developed using Vue.js for the front end and Laravel for the back end, incorporating user authentication, a dashboard for easy management, and the ability to track vehicles via SMS. Existing vehicle tracking systems frequently lack key features such as user management and tracking device management, which the proposed system addresses. Overall, this research provides additional functionalities that meet the contemporary needs of vehicle tracking and management, contributing valuable insights to the field.

INTRODUCTION

The project is focused on developing an ML-enabled automated surveillance and visitor management system for residential societies. The primary goal is to integrate image processing and machine learning to automate the recognition of vehicles, authenticate residents and visitors, and manage access control seamlessly. With the growing demand for secure and efficient surveillance, this project aims to provide a solution that utilizes Automatic Number Plate Recognition (ANPR) and a backend database to visitors. In recent years, advancements in machine learning (ML) and Internet of Things (IoT) technologies have revolutionized the fields of surveillance and vehicle monitoring, especially in urban environments. Societies and cities are facing challenges related to traffic management, security, and real-time monitoring of vehicles to ensure safety and efficiency. Traditional surveillance systems have limitations in terms of scalability, real-time data processing, and automated decision-making. However, with the integration of ML algorithms and IoT-enabled devices, it has become feasible to develop intelligent systems that can monitor, track, and analyze vehicle behavior in real time. These systems not only enhance security but also enable automated speed monitoring, license plate recognition, and

Impact Factor value: 8.315

IoT Based Vehicle Monitoring System [3]: The literature survey reviews various methodologies for vehicle tracking systems that utilize GPS and GSM technologies. One study proposes a public transport monitoring solution using Raspberry Pi and GPS antennas to track vehicle locations. Another focuses on an Arduino-based real-time tracking system designed for personal vehicle

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