Computer Vision for Intelligent Traffic Monitoring and Control

References

[1] Chandrasekhar, M., Saikrishna, C., Chakradhar, B., Kumar, P. P., & Sasanka, C. (2013). Traffic control using digital image processing. International Journal of Advanced Electrical and Electronics Engineering, 2(5), 96-100.

[2] Choudekar, P., Banerjee, S., & Muju, M. K. (2011). Real time traffic light control using image processing. Indian Journal of Computer Science and Engineering (IJCSE), 2(1), 6-10.

[3] De Oliveira, L. F. P., Manera, L. T., & Da Luz, P. D. G. (2020). Development of a smart traffic light control system with real -time monitoring. IEEE Internet of Things Journal, 8(5), 3384-3393.

[4] Dilek, E., & Dener, M. (2023). Computer vision applications in intelligent transportation systems: a survey. Sensors, 23(6), 2938.

[5] Haferkamp, M., Al-Askary, M., Dorn, D., Sliwa, B., Habel, L., Schreckenberg, M., & Wietfeld, C. (2017, June). Radio-based traffic flow detection and vehicle classification for future smart cities. In 2017 IEEE 85th Vehicular Technology Conference (VTC Spring) (pp. 1-5). IEEE.

[6] Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2019). Panoptic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 9404-9413).

[7] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.

[8] Meneguette, R. I., Geraldo Filho, P. R., Bittencourt, L. F., Ueyama, J., Krishnamachari, B., & Villas, L. A. (2015, July). Enhancing intelligence in inter-vehicle communications to detect and reduce congestion in urban centers. In 2015 IEEE Symposium on Computers and Communication (ISCC) (pp. 1-6). IEEE.

[9] Na, Y., Guo, Y., Fu, Q., & Yan, Y. (2015, August). An acoustic traffic monitoring system: Design and implementation. In 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom) (pp. 119-126). IEEE.

[10] Nodado, J. T. G., Morales, H. C. P., Abugan, M. A. P., Olisea, J. L., Aralar, A. C., & Loresco, P. J. M. (2018, October). Intelligent traffic light system using computer vision with android monitoring and control. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 2461-2466). IEEE.

[11] Oliver, K. (2022, 10 21). Convolutional Neural Networks: An Introduction. Medium. URL: https://towardsdatascience.com/from- perceptron-to-densenet-an-introduction-to- convolutional-neural-networks-ab37e3b7872e.

[12] Osman, T., Psyche, S. S., Ferdous, J. S., & Zaman, H. U. (2017, January). Intelligent traffic management system for cross section of roads using computer vision. In 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 1-7). IEEE.

[13] Pandit, V., Doshi, J., Mehta, D., Mhatre, A., & Janardhan, A. (2014). Smart traffic control system using image processing. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 3(1), 2278-6856.

[14] Parveen, S., Chadha, R. S., Noida, C., Kumar, I. P., & Singh, J. (2022). Artificial Intelligence in Transportation Industry. Int. J. Innov. Sci. Res. Technol, 7, 1274-1283.

[15] Prakash, U. E., Vishnupriya, K. T., Thankappan, A., & Balakrishnan, A. A. (2018, July). Density based traffic control system using image processing. In 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research (ICETIETR) (pp. 1-4). IEEE.

[16] Rao, G., Divecha, B., Joshi, J., & Jaiswal, A. (2022). Traffic Light Management System Using Image Processing.

[17] Ravish, R., & Swamy, S. R. (2021). Intelligent traffic management: A review of challenges, solutions, and future perspectives. Transport and Telecommunication Journal, 22(2), 163- 182.

[18] Singh, S., Vishvakarma, M., Yadav, R., & Anavkar, H. (2024). Intelligent Traffic Light Control Using IoT.

[19] Younis, O., & Moayeri, N. (2017). Employing cyber-physical systems: Dynamic traffic light control at road intersections. IEEE Internet of Things Journal, 4(6), 2286-2296.

[20] Zinchenko, V., Kondratenko, G., Sidenko, I., & Kondratenko, Y. (2020, August). Computer vision in control and optimization of road traffic. In 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP) (pp. 249-254). IEEE.

@article{1706535,
      author = {Obi-Obuoha Abiamamela, Rizama Victor Samuel},
      title = {Computer Vision for Intelligent Traffic Monitoring and Control},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {5},
      pages = {392-405},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706535.pdf},
      abstract = {Urban traffic congestion remains a critical challenge, driven by increasing vehicle numbers and limitations in state of art traffic control systems. Conventional traffic management approaches often lack the flexibility to respond dynamically to real-time conditions, leading to increased congestion, greater energy consumption, and prolonged travel times. To address this, this paper proposes an Intelligent Traffic System (ITS) that leverages computer vision to enable real-time traffic control. The system uses Closed Circuit Television (CCTV) cameras strategically placed at traffic intersections to capture live streams, from which vehicle counts in each lane are determined through image processing techniques. Based on these counts, adaptive signal timings are allocated to optimize traffic flow. The results show that the proposed system saved 75.42% of the commute time as compared to a conventional traffic system. This approach not only enhances traffic fluidity but also paves the way for more responsive and efficient urban traffic management infrastructure.},
      keywords = {Computer Vision (CV), Dynamic Traffic Management, Intelligent Traffic System (ITS), Vehicle Detection.},
      month = {November},
  }