Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles
DOI:
https://doi.org/10.13052/jmm1550-4646.1926Keywords:
Deep learning, traffic flow monitoring, unmanned aerial vehicles, video surveillance, vehicle counting, speed estimationAbstract
Recently, intelligent video surveillance technologies using unmanned aerial vehicles (UAVs) have been considerably increased in the transportation sector. Real time collection of traffic videos by the use of UAVs finds useful to monitor the traffic flow and road conditions. Since traffic jams have become common in urban areas, it is needed to design artificial intelligence (AI) based recognition techniques to attain effective traffic flow monitoring. Besides, the traffic flow monitoring system can assist the traffic managers to start efficient dispersal actions. Therefore, this study designs a real time traffic flow monitoring system using deep learning (DL) and UAVs, called RTTFM-DL. The proposed RTTFM-DL technique aims to detect vehicles, count vehicles, estimate speed and determine traffic flow. In addition, an efficient vehicle detection model is proposed by the use of Faster Regional Convolutional Neural Network (Faster RCNN) with Residual Network (ResNet). Also, a detection line based vehicle counting approach is designed, which is based on overlap ratio. Finally, traffic flow monitoring takes place based on the estimated vehicle count and vehicle speed. In order to guarantee the effectual performance of the RTTFM-DL technique, a series of experimental analyses take place and the results are examined under varying aspects. The experimental outcomes highlighted the betterment of the RTTFM-DL technique over the recent techniques. The RTTFM-DL technique has gained improved outcomes with a higher accuracy of 0.975.
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Vlahogianni, E.I., Del Ser, J., Kepaptsoglou, K. and Laña, I., 2021. Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning. Journal of Big Data Analytics in Transportation, 3(1), pp. 1–13.
Barmpounakis EN, Vlahogianni EI, Golias JC (2016) Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges. Int J Transp Sci Technol 5(3):111–122.
Jian, L., Li, Z., Yang, X., Wu, W., Ahmad, A. and Jeon, G., 2019. Combining unmanned aerial vehicles with artificial-intelligence technology for traffic-congestion recognition: electronic eyes in the skies to spot clogged roads. IEEE Consumer Electronics Magazine, 8(3), pp. 81–86.
G. Salvo, L. Caruso, and A. Scordo, “Urban traffic analysis through an UAV,” Procedia-Social Behavioral Sci., vol. 111, pp. 1083–1091, 2014.
J. Wan, Y. Yuan, and Q. Wang, “Traffic congestion analysis: A new perspective,” in Proc. Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 1398–1402.
E. D’Andrea and F. Marcelloni, “Detection of traffic congestion and incidents from GPS trace analysis,” Expert Syst. Applicat., vol. 73, pp. 43–56, 2017.
Ham, S.W., Park, H.C., Kim, E.J., Kho, S.Y. and Kim, D.K., 2020. Investigating the influential factors for practical application of multi-class vehicle detection for images from unmanned aerial vehicle using deep learning models. Transportation Research Record, 2674(12), pp. 553–567.
Varia, N., Dokania, A. and Senthilnath, J., 2018, November. DeepExt: A convolution neural network for road extraction using RGB images captured by UAV. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1890–1895). IEEE.
C Pretty Diana Cyril, J Rene Beulah,Mohan, A Harshavardhan, D Sivabalaselvamani, An automated learning model for sentiment analysis and data classification of Twitter data using balanced CA-SVM, https://doi.org/10.1177/1063293X211031485.
Vlahogianni, E.I., Del Ser, J., Kepaptsoglou, K. and Laña, I., 2021. Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning. Journal of Big Data Analytics in Transportation, 3(1), pp. 1–13.
Zhang, H., Liptrott, M., Bessis, N. and Cheng, J., 2019, September. Real-time traffic analysis using deep learning techniques and uav based video. In 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1–5). IEEE.
Micheal, A.A., Vani, K., Sanjeevi, S. and Lin, C.H., 2021. Object Detection and Tracking with UAV Data Using Deep Learning. Journal of the Indian Society of Remote Sensing, 49(3), pp. 463–469.
S., Berlin, M.A., Tripathi, S. et al. IoT-based traffic prediction and traffic signal control system for smart city. Soft Computing (2021). https://doi.org/10.1007/s00500-021-05896-x
Paulraj, D 2020, ‘An Automated Exploring And Learning Model For Data Prediction Using Balanced CA-SVM’, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, no. 5, April 2020 , DOI: https://doi.org/10.1007/s12652-020-01937-9
Gupta, H. and Verma, O.P., 2021. Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach. Multimedia Tools and Applications, pp. 1–21.
Zhang, J.S., Cao, J. and Mao, B., 2017, July. Application of deep learning and unmanned aerial vehicle technology in traffic flow monitoring. In 2017 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 1, pp. 189–194). IEEE.
Ammour, N., Alhichri, H., Bazi, Y., Benjdira, B., Alajlan, N. and Zuair, M., 2017. Deep learning approach for car detection in UAV imagery. Remote Sensing, 9(4), p. 312.
Ye, D.H., Li, J., Chen, Q., Wachs, J. and Bouman, C., 2018. Deep learning for moving object detection and tracking from a single camera in unmanned aerial vehicles (UAVs). Electronic Imaging, 2018(10), pp. 466–1.
Zhang, Y., Song, C. and Zhang, D., 2020. Deep learning-based object detection improvement for tomato disease. IEEE Access, 8, pp. 56607–56614.
F. Liu, Z. Zeng, and R. Jiang, “A Video-based Real-time Adaptive Vehicle-counting System for Urban Roads,” PLOS ONE 12(11): e0186098, 2017.
https://github.com/civftor/detection-and-tracking-from-uav
Zhu, J., Sun, K., Jia, S., Li, Q., Hou, X., Lin, W., Liu, B. and Qiu, G., 2018. Urban traffic density estimation based on ultrahigh-resolution UAV video and deep neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), pp. 4968–4981.