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.
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Dimililer, K., Ever, Y.K., Mustafa, S.M. (2020). Vehicle Detection and Tracking Using Machine Learning Techniques. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham. https://doi.org/10.1007/978-3-030-35249-3_48
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DOI: https://doi.org/10.1007/978-3-030-35249-3_48
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