Supervising vehicle using pattern recognition
Date
2018-04
Journal Title
Journal ISSN
Volume Title
Publisher
BRAC University
Abstract
Our lives are becoming busier day by day. We are consequently forced to delegate important activities to other people. In developing countries, the middle class often have paid drivers pick up their children from schools. What if the driver decides to deviate from the usual route into a seedy part of town with the child? What if it speeds and is driving recklessly? What if it gets into an accident? In our countries like us, supervising our vehicles when we are not present in it, and being notified if anyone else using it for any unwanted/illegal intention is of paramount importance in our country. Alarms are annoying, and we want to improvise the system in a smarter way for smarter monitoring. The proposed system is developed by applying Linear Regression models, kth-Nearest-Neighbor and Support Vector Machine classifier to identify a pattern and detect abnormal behavior of the vehicle.
Description
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 24-26).
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
Includes bibliographical references (pages 24-26).
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
Keywords
Machine learning, Pattern recognition, KOAD algorithm, KNN algorithm, Linear regression, Support Vector Machine (SVM), Global Positioning System(GPS)
