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Vehicle Detection and Tracking Using Machine Learning Techniques

  • Kamil DimililerEmail author
  • Yoney Kirsal Ever
  • Sipan Masoud Mustafa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1095)

Abstract

More than two decades machine learning techniques have been applied in multidisciplinary fields in order to find more accurate, efficient and effective solutions. This research tries to detect vehicles in images and videos. It deploys a dataset from Udacity in order to train the developed machine learning algorithms. Support Vector Machine (SVM) and Decision Tree (DT) algorithms have been developed for the detection and tracking tasks. Python programming language have been utilized as the development language for the creation and training of both models. These two algorithms have been developed, trained, tested, and compared to each other to specify the weaknesses and strengths of each of them, although to present and suggest the best model among these two. For the evaluation purpose multiple techniques are used in order to compare and identify the more accurate model. The primary goal and target of the paper is to develop a system in which the system should be able to detect and track the vehicles automatically whether they are static or moving in images and videos.

Keywords

Vehicle detection Vehicle tracking SVM Decision tree Image detection Object detection and tracking 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical and Electronic Engineering, Faculty of EngineeringNear East UniversityNicosiaTurkey
  2. 2.Department of Software Engineering, Faculty of EngineeringNear East UniversityNicosiaTurkey
  3. 3.Research Center of Experimental Health SciencesNear East UniversityNicosiaTurkey
  4. 4.Applied Artificial Intelligence Research CentreNear East UniversityNicosiaTurkey
  5. 5.Duhok Polytechnic UniversityDuhokIraq

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