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http://dx.doi.org/10.15207/JKCS.2021.12.2.001

Deep Learning-based Vehicle Anomaly Detection using Road CCTV Data  

Shin, Dong-Hoon (Data Mining Lab., Department of Computer Science, Kyonggi University)
Baek, Ji-Won (Data Mining Lab., Department of Computer Science, Kyonggi University)
Park, Roy C. (Department of Information and Communication Software Engineering, Sangji University)
Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
Publication Information
Journal of the Korea Convergence Society / v.12, no.2, 2021 , pp. 1-6 More about this Journal
Abstract
In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.
Keywords
Road Traffic; Deep Learning; Object Detection; Anomaly Detection; CCTV; Extraction;
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1 X. Lei & Z. Sui. (2019). Intelligent fault detection of high voltage line based on the Faster R-CNN, Measurement, 138, 379-385. DOI : 10.1016/j.measurement.2019.01.072   DOI
2 Y. Jamtsho, P. Riyamongkol & R. Waranusast (2020) Real-time Bhutanese license plate localization using YOLO, ICT Express, 6(2), 121-124. DOI : 10.1016/j.icte.2019.11.001   DOI
3 Z. Tang, M. Naphade, M-Y Liu, X. Yang, S. Birchfield, S. Wang, R. Kumar & D. Anastasiu, J. N. Hwang (2019) Cityflow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification, CVPR, 8797-8806. DOI : 10.1109/cvpr.2019.00900   DOI
4 Z. Zivkovic. (2004). Improved adaptive Gaussian mixture model for background subtraction, In Proc of the 17th International Conference on Pattern Recognition, ICPR, 2, 28-31. DOI : 10.1109/icpr.2004.1333992   DOI
5 Z. Zivkovic & F. van der Heijden. (2006) Efficient adaptive density estimation per image pixel for the task of background subtraction, Pattern Recognition Letters, 27(7), 773-780. DOI : 10.1016/j.patrec.2005.11.005   DOI
6 A. Bochkovskiy, C. Y. Wang & H. Y. M. Liao. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection, arXiv preprint arXiv:2004.10934.
7 H. Yoo & K. Chung. (2020). Deep Learning-based Evolutionary Recommendation Model for Heterogeneous Big Data Integration, KSII Transactions on Internet and Information Systems, 14(9), 3730-3744. DOI : 10.3837/tiis.2020.09.009   DOI
8 D. H. Shin, R. C. Park & K. Chung. (2020). Prediction of Traffic Congestion Based on LSTM Through Correction of Missing Temporal and Spatial Data, IEEE Access, 8, 150784-150796. DOI : 10.1109/access.2020.3016469   DOI
9 C. M. Kim, E. J. Hong, K. Chung & R. C. Park. (2020). Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions, Applied Sciences, 10(8), 2956. DOI : 10.3390/app10082956   DOI
10 D. H. Shin, R. C. Park & K. Chung (2020) Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data, IEEE Access, 8, 108664-108674. DOI : 10.1109/access.2020.3000638   DOI
11 HYUNDAI Tech, https://tech.hyundaimotorgroup.com/
12 T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona & D. Ramanan. (2014). Microsoft coco: Common objects in context, In European conference on computer vision, 740-755. DOI : 10.1007/978-3-319-10602-1_48   DOI
13 S. S. Park, J. W. Baek, S. M. Jo & K. Chung. (2019). Motion Monitoring using Mask R-CNN for Articulation Disease Management, Journal of the Korea Convergence Society, 10(3), 1-6. DOI : 10.15207/JKCS.2019.10.3.001   DOI
14 C. Ma, L. Chen & J. Yong. (2019). AU R-CNN: Encoding expert prior knowledge into R-CNN for action unit detection, Neurocomputing, 355, 35-47. DOI : 10.1016/j.neucom.2019.03.082   DOI
15 J. Li, X. Liang, S. Shen, T. Xu, J. Feng & S. Yan. (2017). Scale-aware fast R-CNN for pedestrian detection, IEEE transactions on Multimedia, 20(4), 985-996. DOI : 10.1109/tmm.2017.2759508   DOI