Zacytuj

1. Burnos, P., Gajda, J., Piwowar, P., Sroka, R., Stencel, M., Zeglen, T. (2007) Measurements of road traffic parameters using inductive loops and piezoelectric sensors.Search in Google Scholar

2. Sun, C., Ritchie, S. G. and Tsai, K. (1998) Algorithm development for derivation of section-related measures of traffic system performance using inductive loop detectors. Transportation Research Record, 1643(1), pp. 171–180.10.3141/1643-21Search in Google Scholar

3. Slimani, I., Zaarane, A., Hamdoun, A., Atouf, I. (2018) Traffic surveillance system for vehicle detection using discrete wavelet transform. Journal of Theoretical & Applied Information Technology, 96(17).Search in Google Scholar

4. AL Okaishi, W., Zaarane, A., Slimani, I., Atouf, I., Benrabh, M. (2019a) Vehicular queue length measurement based on edge detection and vehicle feature extraction. Journal of Theoretical & Applied Information Technology, 97(5).Search in Google Scholar

5. Fathy, M., Siyal, MY. (1995) Real-time image processing approach to measure traffic queue parameters. IEE Proceedings-Vision, Image and Signal Processing, 142(5), pp. 297–303.10.1049/ip-vis:19952064Search in Google Scholar

6. Albiol, A., Albiol, A., Mossi, J. M. (2011a) Video-based traffic queue length estimation. Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on. IEEE, pp. 1928–1932.10.1109/ICCVW.2011.6130484Search in Google Scholar

7. Chintalacheruvu, N., Muthukumar, V. (2012) Video based vehicle detection and its application in intelligent transportation systems. Journal of Transportation Technologies, 2(04), p. 305.10.4236/jtts.2012.24033Search in Google Scholar

8. Albiol, A., Sanchis, L., Albiol, A., Mossi, J. M. (2011b) Detection of parked vehicles using spatiotemporal maps. IEEE Transactions on Intelligent Transportation Systems, 12(4), pp. 1277–1291.10.1109/TITS.2011.2156791Search in Google Scholar

9. Rourke, A., Bell, M. (1991) Queue detection and congestion monitoring using image processing. Traffic engineering & control, 32(9).Search in Google Scholar

10. Siyal, M. Y., Fathy, M. (1999) A neural-vision based approach to measure traffic queue parameters in real-time. Pattern Recognition Letters, 20(8), pp. 761–770.10.1016/S0167-8655(99)00040-9Search in Google Scholar

11. Iwasaki, Y. (1997) An image processing system to measure vehicular queues and an adaptive traffic signal control by using the information of the queues. Intelligent Transportation System, ITSC’97., IEEE Conference on. IEEE, pp. 195–200.10.1109/ITSC.1997.660474Search in Google Scholar

12. Zanin, M., Messelodi, S., Modena, C. M. (2003) An efficient vehicle queue detection system based on image processing. null. IEEE, p. 232.10.1109/ICIAP.2003.1234055Search in Google Scholar

13. Shirazi, M. S., Morris, B. (2015) Vision-based vehicle queue analysis at junctions. Advanced Video and Signal Based Surveillance (AVSS), 2015 12th IEEE International Conference on. IEEE, pp. 1–6.10.1109/AVSS.2015.7301732Search in Google Scholar

14. Sen-Ching, S. C., Kamath, C. (2004) Robust techniques for background subtraction in urban traffic video. Visual Communications and Image Processing. International Society for Optics and Photonics, 5308, pp. 881–893.Search in Google Scholar

15. AL Okaishi, W., Atouf, I., Benrabh, M. (2019b) Real-Time Traffic Light Control System Based on Background Updating and Edge Detection. 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS) on. IEEE, pp. 1–5.10.1109/WITS.2019.8723752Search in Google Scholar

16. Lowe, D. (2004) Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60, pp. 91–110.10.1023/B:VISI.0000029664.99615.94Search in Google Scholar

17. Dalal, N., Triggs, B. (2005) Histograms of oriented gradients for human detection. IEEE computer society conference on computer vision and pattern recognition (CVPR’05), 1, pp. 886–893.10.1109/CVPR.2005.177Search in Google Scholar

18. Tan, X., Triggs, B. (2007) Fusing Gabor and LBP feature sets for kernel-based face recognition. International workshop on analysis and modeling of faces and gestures, pp. 235–249.10.1007/978-3-540-75690-3_18Search in Google Scholar

19. Nogueira, K., Miranda, W. O., Dos Santos, J. A. (2015) Improving spatial feature representation from aerial scenes by using convolutional networks. Graphics, Patterns and Images (SIBGRAPI), 2015 28th SIBGRAPI Conference on, IEEE, pp. 289–296.10.1109/SIBGRAPI.2015.39Search in Google Scholar

20. Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012) Imagenet classification with deep convolutional neural networks. Neural Information Processing Systems, pp. 1106–1114.Search in Google Scholar

21. Simonyan, K., Zisserman, A. (2014) Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: 1409.1556.Search in Google Scholar

22. Girshick, R., Donahue, J., Darrell, T. (2015a) Region-based convolutional networks for accurate object detection and segmentation. IEEE transactions on pattern analysis and machine intelligence, 38(1), pp. 142–158.10.1109/TPAMI.2015.243738426656583Search in Google Scholar

23. Girshick, R. (2015b) Fast R-CNN. IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448.10.1109/ICCV.2015.169Search in Google Scholar

24. Ren, S., HE, K., Girshick, R., Sun, J. (2015) Faster R-CCN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, pp. 91–99.Search in Google Scholar

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