Vol. 22 No. Special Issue 2 (2023): Mapana-Journal of Sciences
Research Articles

Machine Learning based Vehicle Counting and Detection System

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  • Jayamalar T,
  • N. Krishnaveni
Jayamalar T
Avinashilingam Institute for Home Science and Higher Education for Women
N. Krishnaveni
Avinashilingam Institute for Home Science and Higher Education for Women
DOI: https://doi.org/10.12723/mjs.sp2.12

Published 2023-12-27

Keywords

  • Vehicle Detection,
  • YOLOv3,
  • SVM,
  • Histogram,
  • Sliding Window

Copyright (c) 2023

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

The study of how machines perceive instead of humans is known as vehicle detection or computer vision object identification. The primary purpose of a vehicle detection system is to identify one or multiple vehicles within the input images and live video feed. The dataset is used to train image processing algorithms for tasks like detection and tracking. To pinpoint the defects and strength of each image processing system, assessment criteria are used to develop, train, test, and compare them. To recognize, track, and count the vehicle in images and videos, the image processing algorithms such as CNN YOLOv3 and SVM are implemented. The main goal and intention of this work is to develop a system that can intelligently identify and track automobiles in still images and moving movies. The results demonstrated that CNN-based YOLOv3 does a  good job of detecting and tracking vehicles.

 

 

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References

  1. Andrew, W. M. and Victor, M. (2003), Handbook of International Banking(London: Edward Elgar Publishing Limited), 350-358 2)
  2. Bambrick, N. (2018). Support vector machines: A simple explanation. línea]. Disponible en: https://www. kdnuggets. com/2016/07/support-vector-machinessimple-explanation. html.
  3. Basak, D., Pal, S., & Patranabis, D. C. (2007). Support Vector Regression Neural Information Processing–Letters and Reviews.
  4. Berni, J. A., Zarco-Tejada, P. J., Suárez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for
  5. vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on geoscience and Remote Sensing, 47(3),
  6. -738.
  7. Chen, X., & Meng, Q. (2015, November). Robust vehicle tracking and detection from UAVs. In 2015 7th International
  8. Conference of Soft Computing and Pattern Recognition (SoCPaR) (pp. 241-246). IEEE.
  9. Chen, X. (2016). Automatic vehicle detection and tracking in aerial video (Doctoral dissertation, Loughborough University).
  10. Kalghatgi, M. P., Ramannavar, M., & Sidnal, N. S. (2015). A neural network approach to personality prediction based
  11. on the big-five model. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(8), 56-63.
  12. Kanistras, K., Martins, G., Rutherford, M. J., & Valavanis, K. P. (2013, May). A survey of unmanned aerial vehicles (UAVs)
  13. for traffic monitoring. In 2013 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 221-234). IEEE.
  14. Khan, K., Baharudin, B. B., & Khan, A. (2009, June). Mining opinion from text documents: A survey. In 2009 3rd IEEE
  15. International Conference on Digital Ecosystems and Technologies (pp. 217-222). IEEE.
  16. Koza, J. R., Bennett, F. H., Andre, D., & Keane, M. A. (1996). Automated design of both the topology and sizing of analog
  17. electrical circuits using genetic programming. In Artificial intelligence in design’96 (pp. 151-170). Springer, Dordrecht.
  18. Noh, S., Shim, D., & Jeon, M. (2015). Adaptive sliding-window strategy for vehicle detection in highway environments.
  19. IEEE Transactions on Intelligent Transportation Systems, 17(2), 323-335.
  20. Patel, P. J., Patel, N. J., & Patel, A. R. (2014). Factors affecting currency exchange rate, economical formulas and prediction models. International Journal of Application or Innovation in Engineering & Management, 3(3), 53-56.
  21. Faqih, A., Lianto, A. P., & Kusumoputro, B. (2019, January). Mackey-Glass chaotic time series prediction using modified
  22. RBF neural networks. In Proceedings of the 2nd International Conference on Software Engineering and Information
  23. Management (pp. 7- 11).
  24. Razakarivony, S., & Jurie, F. (2016). Vehicle detection in aerial imagery: A small target detection benchmark. Journal
  25. of Visual Communication and Image Representation, 34, 187-203.
  26. Russell, S. J., &Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited.
  27. Sahli, S., Ouyang, Y., Sheng, Y., & Lavigne, D. A. (2010, April). Robust vehicle detection in low-resolution aerial imagery.
  28. In Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII (Vol. 7668, pp. 164-171). SPIE.
  29. Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 1-19.
  30. Susaki, J. (2015). Region-based automatic mapping of tsunami-damaged buildings using multi-temporal aerial images. Natural Hazards, 76(1), 397-420.
  31. Tenti, P. (1996). Forecasting foreign exchange rates using recurrent neural networks. Applied Artificial Intelligence,
  32. (6), 567-582.
  33. The Federal Reserve Board. (2004) “FRB:Speech, Bernanke -- International Monetary Reform and Capital Freedom--
  34. October14, 2004”.
  35. Tsai, Y. C., Chen, J. H., & Wang, J. J. (2018). Predict Forex Trend via Convolutional Neural Networks. arXiv preprintar Xiv:1801.03018.
  36. Tseng, F. M., Tzeng, G. H., Yu, H. C., & Yuan, B. J. (2001). Fuzzy ARIMA model for forecasting the foreign exchange
  37. market. Fuzzy sets and systems, 118(1), 9-19.
  38. Van Gerven, M., & Bohte, S. (2017). Artificial neural networks as models of neural information processing. Frontiers in
  39. Computational Neuroscience, 11, 114.
  40. Viola, P., Jones, M. J., & Snow, D. (2005). Detecting pedestrians using patterns of motion and appearance. International
  41. Journal of Computer Vision, 63(2), 153-161.
  42. Vyklyuk, Y., Vukovic, D., & Jovanovic, A. (2013). Forex prediction with neural network: USD/EUR currency pair.
  43. Актуальні проблеми економіки, (10), 261-273
  44. Wang, X., Zhu, H., Zhang, D., Zhou, D., & Wang, X. (2014). Vision-based detection and tracking of a mobile ground
  45. target using a fixed-wing UAV. International Journal of -Advanced Robotic Systems, 11(9), 156.