Vehicle Detection And Vehicle Tracking Applications On Traffic Video Surveillance Systems: A systematic literature review
DOI:
https://doi.org/10.22399/ijcesen.629Keywords:
Vehicle detection, Vehicle tracking, Artificial Intelligence, Deep LearningAbstract
The number of vehicles in traffic and the use of traffic surveillance systems are increasing day by day. This situation has revealed the necessity of control and analysis processes on traffic surveillance systems. Vehicle detection and vehicle tracking studies for the purpose of analyzing video sequences obtained from surveillance systems have recently become a popular field of study. Despite the increase in studies in this field, the aimed level has not been reached. Many reasons such as weather changes, day-night difference, vehicles blocking each other in traffic, background complexity make vehicle detection and tracking difficult. This study is presented to guide researchers who want to work in the field. In order to determine the common trends of the studies and to analyze the studies, a data set was created by searching the Web of Science database using the keywords "vehicle detection" and "vehicle tracking". In order to analyze the obtained data, the Voswiever (version 1.6.20) program and the R studio programs "bibliometrix" package and the biblioshiny application were used.
References
Kulkarni A. P.,& Baligar, V. P. (2020). Real Time Vehicle Detection, Tracking and Counting Using Raspberry-Pi, 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, pp. 603-607, doi: 10.1109/ICIMIA48430.2020.9074944.
Aqel, S. , Hmimid, A. , Sabri, M. A.,& Aarab, A. (2017). Road traffic: Vehicle detection and classification, 2017 Intelligent Systems and Computer Vision (ISCV).
Li, D., Liang, B, & Zhang, W. (2014, April). Real-time moving vehicle detection, tracking, and counting system implemented with OpenCV. In 2014 4th IEEE international conference on information science and technology (pp. 631-634). IEEE.
Maqbool, S., Khan, M., Tahir, J., Jalil, A., Ali, A.,& Ahmad, J. (2018, July). Vehicle detection, tracking and counting. In 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) (pp. 126-132). IEEE.
Nixon, M. S.,&Aguado, A. S. (2012). Low-level feature extraction (including edge detection). Feature extraction & image processing for computer vision, 137-216.
Azimjonov, J.,& Özmen, A. (2021). A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways. Advanced Engineering Informatics, 50, 101393.
Chauhan, N.K.,& Singh, K. (2018). A review on conventional machine learning vs deep learning, 2018 International Conference on Computing, Power and Communication Technologies (GUCON) , pp. 347-352
.Datondji, S.R.E., Dupuis, Y., Subirats, P. ,& Vasseur, P. (2016), A survey of vision-based traffic monitoring of road intersections, IEEE Trans. Intell. Transp. Syst., 17 (10); 2681-2698
Wang, Y. (2020). Moving vehicle detection and tracking based on video sequences. Traitement du Signal, 37(2);325-331. https://doi.org/10.18280/ts.370219
]Hwang, J., Huh,K., & Lee, D.(2009) Vision-based vehicle detection and tracking algorithm design, Optical Engineering. 48(12); 127201. https://doi.org/10.1117/1.3269685
Chong,Y., Chen, W., Li,Z., Lam, W. H.K. , Zheng, C.,& Li, Q. (2013), Integrated real-time vision-based preceding vehicle detection in urban roads. Neurocomputing. 116;144-149.
https://doi.org/10.1016/j.neucom.2011.11.036.
Ashraf, M.H., Jabeen, F., Alghamdi, H., Zia, M.S., & Almutairi, M. S. (2023), HVD-Net: A Hybrid Vehicle Detection Network for Vision-Based Vehicle Tracking and Speed Estimation. Journal of King Saud University - Computer and Information Sciences,35(8);101657. https://doi.org/10.1016/j.jksuci.2023.101657.
Lopes, R. M., Fidalgo-Neto, A.A., & Mota,F.B. (2017). Facebook in educational research: a bibliometric analysis. Scientometrics, 111(3);1591-1621.
Üsdiken, B., & Pasadeos, Y. (1993). Türkiye’de örgütler ve yönetim yazını. Amme İdaresi Dergisi, 26(2);73-93
Zeren, D., & Kaya, N. (2020). Digital Marketing: A Bibliometric Analysis of National Literature. Çağ University Journal of Social Sciences. 17(1);35-52.
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