Affiliation:
1. Department of Informatics Engineering , Dian Nuswantoro University , 207 Imam Bonjol Street, Semarang 50131, Indonesia
Abstract
Abstract
This research proposes a background subtraction method with the truncate threshold to improve the accuracy of vehicle detection and tracking in real-time video streams. In previous research, vehicle detection accuracy still needs to be optimized, so it needed to be improved. In the vehicle detection method, there are several parts that greatly affect, one of which is the thresholding technique. Different thresholding methods can affect the results of the background and foreground separation. Based on the results of testing the proposed method can improve accuracy by more than 20% compared to the previous method. The thresholding method has a considerable influence on the final result of vehicle object detection. The results of the average accuracy of the three types of time, i.e. morning, daytime, and afternoon reached 96.01%. These results indicate that the vehicle counting accuracy is very satisfying, moreover, the method has also been implemented in a real way and can run smoothly.
Subject
Computer Science Applications,General Engineering
Reference21 articles.
1. 1. Arinaldi, A., Pradana, J. A., & Gurusinga, A. A. (2018). Detection and classification of vehicles for traffic video analytics. Procedia Computer Science, 144, 259–268. DOI:10.1016/j.procs.2018.10.52710.1016/j.procs.2018.10.527
2. 2. Badan Pusat Statistik. (2017). Perkembangan Jumlah Kendaraan Bermotor Menurut Jenis, 1949-2017. DOI:10.1055/s-2008-104032510.1055/s-2008-1040325
3. 3. Badan Pusat Statistik. (2020). Sensus Penduduk 2020 - Satu Data Kependudukan Indonesia. Retrieved January 6, 2020, from https://www.bps.go.id/sp2020/slide-1.html#slide=7
4. 4. Barcellos, P., Bouvié, C., Escouto, F. L., & Scharcanski, J. (2015). A novel video based system for detecting and counting vehicles at user-defined virtual loops. Expert Systems with Applications, 42(4), 1845–1856. DOI:10.1016/j.eswa.2014.09.04510.1016/j.eswa.2014.09.045
5. 5. Bouwmans, T., Javed, S., Sultana, M., & Jung, S. K. (2019, September 1). Deep neural network concepts for background subtraction: A systematic review and comparative evaluation. Neural Networks, Vol. 117, pp. 8–66. DOI:10.1016/j.neunet.2019.04.02410.1016/j.neunet.2019.04.024
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