Convolutional neural network for recognizing highway traffic congestion

References

• Anastasi, G., Antonelli, M., Bechini, A., Brienza, S., D’Andrea, E., De Guglielmo, D., Ducange, P., Lazzerini, B., Marcelloni, F., Segatori, A. (2013). Urban and social sensing for sustainable mobility in smart cities. In 2013 Sustainable Internet and ICT for Sustainability (sustainIT), (pp. 1–4). doi:10.1109/SustainIT.2013.6685198
   Google Scholar
• Andrews Sobral, L. O., Schnitman, L., & De Souza, F. (2013). Highway traffic congestion classification using holistic properties. [Paper presentation]. 10th IASTED International Conference on Signal Processing, Pattern Recognition and Applications. doi:10.2316/2013.798-105
   Google Scholar
• Bacon, J., Bejan, A. I., Beresford, A. R., Evans, D., Gibbens, R. J., & Moody, K. (2011). Using real-time road traffic data to evaluate congestion. In Dependable and historic computing. (pp. 93–117). Berlin, Germany: Springer.
   Google Scholar
• Calderoni, L., Maio, D., & Rovis, S. (2014). Deploying a network of smart cameras for traffic monitoring on a “city kernel. Expert Systems with Applications, 41(2), 502–507. doi:10.1016/j.eswa.2013.07.076
   Web of Science ®Google Scholar
• Chan, A. B., & Vasconcelos, N. (2005). Classification and retrieval of traffic video using auto-regressive stochastic processes. In 2005 IEEE Proceedings of Intelligent Vehicles Symposium (pp. 771–776).
   Google Scholar
• Cheng, Q., Liu, Z., Liu, F., & Jia, R. (2017). Urban dynamic congestion pricing: an overview and emerging research needs. International Journal of Urban Sciences, 21 (sup1), 3–18. doi:10.1080/12265934.2016.1227275
   Web of Science ®Google Scholar
• Choudekar, P., Banerjee, S., & Muju, M. (2011). Implementation of image processing in real time traffic light control. In 2011 3rd International Conference on Electronics Computer Technology (ICECT) (Vol. 2, pp. 94–98). doi:10.1109/ICECTECH.2011.5941662
   Google Scholar
• Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 248–255).
   Google Scholar
• Derpanis, K. G., & Wildes, R. P. (2010). Dynamic texture recognition based on distributions of spacetime oriented structure. In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 191–198). doi:10.1109/CVPR.2010.5540213
   Google Scholar
• Derpanis, K. G., & Wildes, R. P. (2011). Classification of traffic video based on a spatiotemporal orientation analysis. In 2011 IEEE Workshop on Applications of Computer Vision (WACV), (pp. 606–613). doi:10.1109/WACV.2011.5711560
   Google Scholar
• Ding, D., Tong, J., & Kong, L. (2019). A deep learning approach for quality enhancement of surveillance video. Journal of Intelligent Transportation Systems, 1–11. doi:10.1080/15472450.2019.1670659
   Web of Science ®Google Scholar
• Gonçalves, W. N., Machado, B. B., & Bruno, O. M. (2012). Spatiotemporal Gabor filters: a new method for dynamic texture recognition. arXiv Preprint arXiv:1201.3612
   Google Scholar
• Hoummady, B. (2002). April 2). Method and device for managing road traffic using a video camera as data source. Google Patents. (US Patent 6,366,219)
   Google Scholar
• Hu, S., Wu, J., & Xu, L. (2012). Real-time traffic congestion detection based on video analysis. Journal of Information & Computational Science, 9(10), 2907–2914.
   Google Scholar
• Kerner, B. S. (2009). Introduction to modern traffic flow theory and control: the long road to three-phase traffic theory. Berlin, Germany: Springer Science & Business Media.
   Google Scholar
• Khasanova, R., & Frossard, P. (2017). Graph-based isometry invariant representation learning. arXiv preprint arXiv:1703.00356
   Google Scholar
• Khattak, Z. H., Magalotti, M. J., & Fontaine, M. D. (2020). Operational performance evaluation of adaptive traffic control systems: A bayesian modeling approach using real-world GPS and private sector probe data. Journal of Intelligent Transportation Systems, 24(2), 156–115. doi:10.1080/15472450.2019.1614445
   Web of Science ®Google Scholar
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. (pp. 1097–1105).
   Google Scholar
• Law, S., Seresinhe, C. I., Shen, Y., & Gutierrez-Roig, M. (2020). Street-frontage-net: urban image classification using deep convolutional neural networks. International Journal of Geographical Information Science, 34(4), 681–627. doi:10.1080/13658816.2018.1555832
   Web of Science ®Google Scholar
• Lempitsky, V., & Zisserman, A. (2010). Learning to count objects in images. In Advances in neural information processing systems. (pp. 1324–1332).
   Google Scholar
• Prabhakar, C. (n.d.). Model based techniques for vehicle tracking in traffic video using spatial local feature.
   Google Scholar
• Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems. (pp. 91–99).
   Google Scholar
• Sadollah, A., Gao, K., Zhang, Y., Zhang, Y., & Su, R. (2019). Management of traffic congestion in adaptive traffic signals using a novel classification-based approach. Engineering Optimization, 51(9), 1509–1528. doi:10.1080/0305215X.2018.1525708
   Web of Science ®Google Scholar
• Shirani, R., Hendessi, F., & Gulliver, T. A. (2009). Store-carry-forward message dissemination in vehicular ad-hoc networks with local density estimation. In 2009 IEEE 70th Vehicular Technology Conference Fall (pp. 1–6).
   Google Scholar
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
   Google Scholar
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–9).
   Google Scholar
• Thianniwet, T., Phosaard, S., & Pattara-Atikom, W. (2009). Classification of road traffic congestion levels from gps data using a decision tree algorithm and sliding windows. In Proceedings of the World Congress on Engineering (Vol. 1, pp. 1–3).
   Google Scholar
• Toropov, E., Gui, L., Zhang, S., Kottur, S., & Moura, J. M. (2015). Traffic flow from a low frame rate city camera. In 2015 IEEE International Conference on Image Processing (ICIP) (pp. 3802–3806). doi:10.1109/ICIP.2015.7351516
   Google Scholar
• Venkatesvara Rao, N., Venkatavara Prasad, D., & Sugumaran, M. (2018). Real-time video object detection and classification using hybrid texture feature extraction. International Journal of Computers and Applications, 1–8. doi:10.1080/1206212X.2018.1525929
   Google Scholar
• Wang, H., Quan, W., & Ochieng, W. Y. (2019). Smart road stud based two-lane traffic surveillance. Journal of Intelligent Transportation Systems, 1–14.
   Google Scholar
• Wang, Q., Wan, J., & Yuan, Y. (2018). Locality constraint distance metric learning for traffic congestion detection. Pattern Recognition, 75, 272–281. doi:10.1016/j.patcog.2017.03.030
   Web of Science ®Google Scholar
• Wei, L., & Hong-Ying, D. (2016). Real-time road congestion detection based on image texture analysis. Procedia Engineering, 137, 196–201. doi:10.1016/j.proeng.2016.01.250
   Google Scholar
• Willis, C., Harborne, D., Tomsett, R., & Alzantot, M. (2017). A deep convolutional network for traffic congestion classification. In Proc NATO IST-158/RSM-010 Specialists’ Meeting on Content Based Real-Time Analytics of Multi-Media Streams (pp. 1–11).
   Google Scholar
• Xia, F., & Zhang, S. (2016). Block-coordinate frank-wolfe optimization for counting objects in images. In Advances in neural information processing systems workshops. (Vol. 2).
   Google Scholar
• Yong-Chuan, Z., Xiao-Qing, Z., Li-Ting, Z., & Zhen-Ting, C. (2011). Traffic congestion detection based on gps floating-car data. Procedia Engineering, 15, 5541–5546. doi:10.1016/j.proeng.2011.08.1028
   Google Scholar
• Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European Conference on Computer Vision (ECCV), (pp. 818–833).
   Google Scholar
• Zhan, C., Duan, X., Xu, S., Song, Z., & Luo, M. (2007). An improved moving object detection algorithm based on frame difference and edge detection. In 4th International Conference on Image and Graphics (ICIG), 2007 (pp. 519–523).
   Google Scholar
• Zhang, Q., & Zhu, S. (2018). Visual interpretability for deep learning: a survey. Frontiers of Information Technology & Electronic Engineering, 19(1), 27–39. doi:10.1631/FITEE.1700808
   Web of Science ®Google Scholar
• Zhang, S., Wu, G., Costeira, J. P., & Moura, J. M. (2017). Understanding traffic density from large-scale web camera data. arXiv Preprint arXiv:1703.05868
   Google Scholar