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Volume 11 | Issue 4 | Year 2021 | Article Id. IJCOT-V11I4P301 | DOI : https://doi.org/10.14445/22492593/IJCOT-V11I4P301
Traffic Violations Detection Review based on Intelligent Surveillance Systems
Hasan Thabit Rashid, Prof. Dr. Israa Hadi Ali
Received | Revised | Accepted |
---|---|---|
20 May 2021 | 23 Jun 2021 | 08 Jul 2021 |
Citation :
Hasan Thabit Rashid, Prof. Dr. Israa Hadi Ali, "Traffic Violations Detection Review based on Intelligent Surveillance Systems," International Journal of Computer & Organization Trends (IJCOT), vol. 11, no. 4, pp. 1-9, 2021. Crossref, https://doi.org/10.14445/22492593/IJCOT-V11I4P301
Abstract
Currently, IT develops our life superficially and quickly has become faster and more complicated. However, This paper offers a brief study of previous techniques for violation of vehicles on surveillance systems expressed by suitable processing methodologies to intelligent surveillance techniques (such as Wi-Fi sensors, image processing, machine learning, and object detection based on appearance and motion) and the use of different types of cameras networks(fixed, motorize, and PTZ) and area topologies(efficient FOVs). This study provides a quick look at various techniques that alert individuals and users to vehicle anomalous movements in the environments of traffic and intelligent systems.
Keywords
computer vision, intelligent surveillance systems,multi-camera networks, traffic control, video tracking.
References
[1]
Nilsson, F. Intelligent Network Video: Understanding Modern Video Surveillance
Systems. CRC Press. Second edition. (2017).
[2] Aghajan, H., &Cavallaro, A. (Eds.). Multi-camera
networks: principles and applications. Academic Press. (2009).
[3] Pandey, S., Jain, R., & Kumar, S. An Efficient Data
Aggregation Algorithm with Gossiping for Smart Transportation System. In
International Conference on Communication, Networks, and Computing (191-200).
Springer, Singapore. (2018).
[4] Ahmad, N., O`Nils, M., &Lawal, N. A taxonomy of visual
surveillance systems. (2013).
[5] Kolekar, M. H. Intelligent Video Surveillance Systems: An
Algorithmic Approach. CRC Press. (2018).
[6] Khan, M. U. K., Shafique, M., & Henkel, J. Energy Efficient
Embedded Video Processing Systems: A Hardware-Software Collaborative Approach.
Springer. (2017).
[7] Ahmed, S. H., Yaqub, M. A., Bouk, S. H., & Kim, D.
SmartCop: Enabling smart traffic violations ticketing in vehicular named data
networks. Mobile Information Systems, (2016).
[8] Aarthy, D. K., Vandanaa, S., Varshini, M., &Tijitha, K.
Automatic identification of traffic violations and theft avoidance. In 2016
Second International Conference on Science Technology Engineering and
Management (ICONSTEM) (2016) (72-76). IEEE.
[9] Mehboob, F., Abbas, M., & Rauf, A. Mathematical
model-based traffic violations identification. Computational and Mathematical
Organization Theory,(2018) 1-17.
[10] Sarikan, S. S., &Ozbayoglu, A. M. Anomaly Detection in
Vehicle Traffic with Image Processing and Machine Learning. Procedia Computer
Science, 140 (2018) 64-69.
[11] Yang, Z., & Pun-Cheng, L. S. Vehicle detection in
intelligent transportation systems and its applications under varying
environments: A review. Image and Vision Computing, 69 (2018) 143-154.
[12] Kurmasha, H. T. R., Alharan, A. F. H., Der, C. S.,
&Azami, N. H. Enhancement of Edge-based Image Quality Measures Using
Entropy for Histogram Equalization-based Contrast Enhancement Techniques.
Engineering, Technology & Applied Science Research, 7(6) (2017) 2277-2281.
[13] Sharma, K. Feature-based efficient vehicle tracking for a
traffic surveillance system. Computers & Electrical Engineering, 70 (2018)
690-701.
[14] Maggio, E., &Cavallaro, A. Video tracking: theory and practice.
John Wiley & Sons. (2011).
[15] Zheng, H., Chang, W., & Wu, J. Traffic flow monitoring
systems in smart cities: Coverage and distinguishability among vehicles.
Journal of Parallel and Distributed Computing, 127 (2019)224-23.
[16] Kurmasha, H. T. R., &Alharan, A. F. A Review and New
Subjective Evaluation Experiment of Objective Metrics used to Evaluate
Histogram Equalization-based Contrast Enhancement Techniques. (2017)
[17] Rashid, H. T., & Ali, I. H., Multi-Camera Collaborative
Network Experimental Study Design of Video Surveillance System for Violated
Vehicles Identification. In Journal of Physics: Conference Series 1879(2)
022090. IOP Publishing. (2021).