Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2016
Title: Anomaly detection in vehicle traffic with image processing and machine learning
Authors: Sarıkan, Selim S.
Özbayoğlu, Ahmet Murat
142991
Keywords: Vehicles
Classification (of information)
vehicle logo
Issue Date: 2018
Publisher: Elsevier B.V.
Source: Sarikan, S. S., & Ozbayoglu, A. M. (2018). Anomaly Detection in Vehicle Traffic with Image Processing and Machine Learning. Procedia Computer Science, 140, 64-69.
Abstract: Anomaly detection is an important part of an Intelligent Transportation System. In this study, image processing and machine learning techniques are used to detect anomalies in vehicle movements. These anomalies include standing and traveling in reverse direction. Images are captured using CCTV cameras from front and rear side of the vehicle. This capability makes the results robust to the variations in operational and environmental conditions. Multiple consecutive frames are acquired for motion detection. Features such as edges and license plate corner locations are extracted for tracking purposes. Direction of the traffic flow is obtained from the trained classifier. K-nearest neighbor is chosen as the classifier model. The proposed method is evaluated on a public highway and promising detection results are achieved. © 2018 The Authors. Published by Elsevier B.V.
Description: Complex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning (2018 : United States)
URI: https://www.sciencedirect.com/science/article/pii/S1877050918319665?via%3Dihub
https://hdl.handle.net/20.500.11851/2016
ISSN: 18770509
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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