A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees
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2021
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Institute of Electrical and Electronics Engineers
Abstract
Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. Our approach included creating a detection model, followed by anomaly detection and analysis. YOLOv5 served as the foundation for our detection model. The anomaly detection and analysis step entail traffic scene background estimation, road mask extraction, and adaptive thresholding. Candidate anomalies were passed through a decision tree to detect and analyze final anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4 score of 0.5686, per the experimental validation.
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This is a manuscript of a proceeding published as A. Aboah, M. Shoman, V. Mandal, S. Davami, Y. Adu-Gyamfi and A. Sharma, "A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees," 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA, 2021, pp. 4202-4207, doi: 10.1109/CVPRW53098.2021.00475. © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.