References
- 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.2018.10.527
- 2. Badan Pusat Statistik. (2017). Perkembangan Jumlah Kendaraan Bermotor Menurut Jenis, 1949-2017. DOI:10.1055/s-2008-1040325
- 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. 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.2014.09.045
- 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.2019.04.024
- 6. Bradski, G., & Kaehler, A. (2008). Learning OpenCV (First Edit; M. Loukides, Ed.). Retrieved from https://www.bogotobogo.com/cplusplus/files/OReillyLearningOpenCV.pdf
- 7. Davis, S. C., Williams, S. E., & Boundy, R. G. (2018). Transportation Energy Data Book. Retrieved from https://tedb.ornl.gov/
- 8. Fernández-Sanjurjo, M., Bosquet, B., Mucientes, M., & Brea, V. M. (2019). Real-time visual detection and tracking system for traffic monitoring. Engineering Applications of Artificial Intelligence, 85, 410–420. DOI:10.1016/j.engappai.2019.07.005
- 9. Gaidash, V., & Grakovski, A. (2016). “Mass Centre” Vectorization Algorithm for Vehicle’s Counting Portable Video System. Transport and Telecommunication Journal, 17(4), 289–297. Retrieved from https://content.sciendo.com/view/journals/ttj/17/4/article-p289.xml?rskey=vMNxen&result=6
- 10. Huang, D.-Y., Chen, C.-H., Hu, W.-C., Yi, S.-C., & Lin, Y.-F. (2012). Feature-based vehicle flow analysis and measurement for a real-time traffic surveillance system. 3(3), 282–296.
- 11. Huang, D. Y., Chen, C. H., Chen, T. Y., Hu, W. C., & Feng, K. W. (2017). Vehicle detection and inter-vehicle distance estimation using single-lens video camera on urban/suburb roads. Journal of Visual Communication and Image Representation, 46, 250–259. DOI:10.1016/j.jvcir.2017.04.006
- 12. Lei, M., Lefloch, D., Gouton, P., & Madani, K. (2008). A video-based real-time vehicle counting system using adaptive background method. SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems, 523–528. DOI:10.1109/SITIS.2008.91
- 13. Mandellos, N. A., Keramitsoglou, I., & Kiranoudis, C. T. (2011). A background subtraction algorithm for detecting and tracking vehicles. Expert Systems with Applications, 38(3), 1619–1631. DOI:10.1016/j.eswa.2010.07.083
- 14. Mohana, H. S., Ashwathakumar, M., & Shivakumar, G. (2009). Vehicle detection and counting by using real time traffic flux through differential technique and performance evaluation. Proceedings - International Conference on Advanced Computer Control, ICACC 2009, 791–795. DOI:10.1109/ICACC.2009.149
- 15. Moutakki, Z., Ouloul, I. M., Afdel, K., & Amghar, A. (2017). Real-Time Video Surveillance System for Traffic Management with Background Subtraction Using Codebook Model and Occlusion Handling. Transport and Telecommunication Journal, 18(4), 297–306. Retrieved from https://content.sciendo.com/view/journals/ttj/18/4/article-p297.xml?rskey=vMNxen&result=8
- 16. Pun, C. M., & Lin, C. (2016). A real-time detector for parked vehicles based on hybrid background modeling. Journal of Visual Communication and Image Representation, 39, 82–92. DOI:10.1016/j.jvcir.2016.05.009
- 17. Setiadi, D. R. I. M., Fratama, R. R., Partiningsih, N. D. A., Rachmawanto, E. H., Sari, C. A., & Andono, P. N. (2019). Real-time multiple vehicle counter using background subtraction for traffic monitoring system. Proceedings - 2019 International Seminar on Application for Technology of Information and Communication: Industry 4.0: Retrospect, Prospect, and Challenges, ISemantic 2019, 23–27. DOI:10.1109/ISEMANTIC.2019.8884277
- 18. Sperling, D., & Gordon, D. (2008). Two Billion Cars Transforming a Culture.
- 19. Sun, M., Wang, Y., Li, T., Lv, J., & Wu, J. (2017). Vehicle counting in crowded scenes with multi-channel and multi-task convolutional neural networks. Journal of Visual Communication and Image Representation, 49, 412–419. DOI:10.1016/j.jvcir.2017.10.002
- 20. Trnovszký, T., Sýkora, P., & Hudec, R. (2017). Comparison of Background Subtraction Methods on Near Infra-Red Spectrum Video Sequences. Procedia Engineering, 192, 887–892. DOI:10.1016/j.proeng.2017.06.153
- 21. Yang, Z., & Pun-Cheng, L. S. C. (2018, January 1). Vehicle detection in intelligent transportation systems and its applications under varying environments: A review. Image and Vision Computing, Vol. 69, pp. 143–154. DOI:10.1016/j.imavis.2017.09.008