Abstract
Lately the number of vehicles circulating on roads had been widely increased especially in the big cities, leading instantly to congestion and consequently the accidents rate increasement. In order to reduce traffic issues and enhance road safety, so many researchers focus their vision on this era problem trying to look for solutions by developing algorithms and embedded applications based on image processing and artificial intelligence, therefore we aim to automate the traditional detecting systems used to spot traffic violations such as: speeding detection; crossing a red light; driving on a prohibited lane, etc. this offenses considered among the principal factors of road accidents, for this main reason the built of those embedded systems play a very important role for traffic accident reduction through monitoring the road safety and automating the penalty detection. In this article, we are trying to study the most recent techniques based on computer vision to monitor and detect traffic offenses, lightning the strengths and weaknesses of these techniques, additionally we provide research a comparison and study basis to facilitate their future work also improve robust approaches to detect road violations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mbarek, A., Jiber, M., Yahyaouy, A., Sabri, A.: Road traffic mortality in Morocco: analysis of statistical data. In: 2020 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco (2020)
Saha, S., Basu, S., Nasipuri, M., Basu, D.K.: Development of an automated Red Light Violation Detection System (RLVDS) for Indian vehicles. In: IEEE National Conference on Computing and Communication Systems, Burdwan (2009)
Wonghabut, P., Ung-arunyawee, R., Satiennam, T.: Traffic light color identification for automatic traffic light violation detection system. In: IEEE International Conference on Engineering, Applied Sciences, and Technology, Phuket, Thailand (2018)
Chen, Y., Yang, C.: Vehicle red-light violation detection base on region. In: 3rd International Conference on Computer Science and Information Technology, Chengdu, China (2010)
Bordia, B., Nishanth, N.: Automated traffic light signal violation detection system using convolutional neural network. In: Soft Computing: Theories and Applications, pp. 579–592 (2020)
Prakash, N., Kamath, C.U., Gururaja, H.S.: Detection of traffic violations using moving object and speed detection. Wutan Huatan Jisuan Jishu XVI(VI), 1001–1749 (2020)
de Goma, J.C., Bautista, R.J., Eviota, M.A.J.: Detecting Red-Light Runners (RLR) and speeding violation through video capture. In: IEEE 7th International Conference on Industrial Engineering and Applications, Bangkok, Thailand (2020)
Franklin, R.J., Mohana: Traffic signal violation detection using artificial intelligence and deep learning. In: Proceedings of the Fifth International Conference on Communication and Electronics Systems. IEEE, Coimbatore (2020)
Peiris, A., Edirisuriya, E.A.T.A., Athuraliya, C.D.: Computer vision based approach for traffic violation detection. In: 13th International Research Conference, Sri Lanka (2020)
Mampilayil, H.R., Rahamathullah, K.: Deep learning-based detection of one-way traffic rule violation of three-wheeler vehicles. In: Proceedings of the International Conference on Intelligent Computing and Control Systems. IEEE, Madurai (2019)
Aqel, S., Hmimid, A., Sabri, M.A., Aarab, A.: Road traffic: vehicle detection and classification. In: 2017 Intelligent Systems and Computer Vision (ISCV), Fez, Morocco (April 2017)
Coco Dataset Dataset. http://cocodataset.org
Behrendt, K., Novak, L., Botros, R.: A deep learning approach to traffic lights: detection, tracking, and classification. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1370–1377 (May 2017)
Heidelberg Collaboratory: Bosch Small Traffic Dataset (2017). https://hci.iwr.uni-heidelberg.de
Lee, Y., et al.: Fast detection of objects using a YOLOv3 network for a vending machine. In: IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Taiwan (2019)
Cucchiara, R., Grana, C., Piccardi, M.: Detecting objects, detecting objects, shadows, and ghosts in video streams by exploiting color and motion information. In: International Conference on Image Analysis and Processing (ICIAP) (2001)
Chengzhong, Y., Jun, Z., Yuan, X.: Video object detection based on background subtraction. J. Southeast Univ. (Nat. Sci. Ed.) 35(Sup (II)) (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
El Manaa, I., Benjelloun, F., Sabri, M.A., Yahyaouy, A., Aarab, A. (2022). Road Traffic Anomaly Detection: A Survey. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-01942-5_77
Download citation
DOI: https://doi.org/10.1007/978-3-031-01942-5_77
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-01941-8
Online ISBN: 978-3-031-01942-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)