References
- 1. Zhou, Y., Nejati, H., Do, T., Cheung, N., Cheah, L. (2019) Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study. Available online: https://arxiv.org/pdf/1601.01145.pdf (accessed on 05 May 2019).
- 2. Biswas, D., Su, H., Wang, C., Blankenship., J., Stevanovic, A. (2017) An Automatic Car Counting System Using OverFeat Framework. Sensors (Basel). 2017 July, 17(7): 1535. Published online 2017 June 30. DOI: 10.3390/s17071535.10.3390/s17071535553951428665360
- 3. Zhang, F., Li, C., Yang, F. (2019) Vehicle Detection in Urban Traffic Surveillance Images Based on Convolutional Neural Networks with Feature Concatenation. Sensors 2019, 19(3), 594, https://doi.org/10.3390/s19030594.10.3390/s19030594638709530704152
- 4. Girshick, R., Donahue, J., Darrell, T., Malik, J. (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 23–28 June 2014, pp. 580–587.10.1109/CVPR.2014.81
- 5. Uijlings, J.R.R., Sande, K.E.A., Gevers, T., Smeulders, A.W.M. (2013) Selective Search for Object Recognition. Int. J. Comput. Vis. 2013, 104, 154–171.
- 6. Wang, K., Wang, R., Feng, Y., Zhang, H., Huang, Q., Jin, Y., Zhang, Y. (2014) Vehicle recognition in acoustic sensor networks via sparse representation, in IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2014, pp. 1–4.
- 7. Kim, H., Song, B. (2013) Vehicle recognition based on radar and vision sensor fusion for automatic emergency braking, in 13th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2013, pp. 1342–1346.
- 8. McKay, T., Salvaggio, C., Faulring, J., Salvaggio, F., McKeown, D., Garrett, A., Coleman, D., Koffman, L. (2012) Passive detection of vehicle loading, in IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2012, pp. 830511–830511.
- 9. Mishra, P., Banerjee, B. 2013) Multiple kernel based KNN classifiers for vehicle classification, International Journal of Computer Applications, Vol. 71, No. 6, 2013.
- 10. Tang, T., Thou, S., Dag, Z., Lei, L., Zou, H. (2017) Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks. Remote Sens. 2017, 9(11), 1170, https://doi.org/10.3390/rs9111170.10.3390/rs9111170
- 11. Zhang, J., Huang, M., Jin, X., Li, X. (2017) A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2. Algorithms 2017, 10, 127.10.3390/a10040127
- 12. Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016) You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 26 June–1 July 2016.10.1109/CVPR.2016.91
- 13. Redmon, J., Farhadi, A. (2016) YOLO9000: Better, Faster, Stronger. arXiv, 2016, arXiv:1612.08242v1.10.1109/CVPR.2017.690
- 14. Tang, T., Zhou, S., Deng, Z., Zou, H., Lei, L. (2017) Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining. Sensors 2017, 17, 336.10.3390/s17020336533596028208587
- 15. Deng, Z., Sun, H., Zhou, S., Zhao, J., Zou, H. (2017) Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled Region-Based Convolutional Neural Networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, 10, 3652–3664.
- 16. Qu, T., Zhang, Q., Sun, S. (2017) Vehicle detection from high-resolution aerial images using spatial pyramid pooling-based deep convolutional neural networks. Multimedia Tools Appl. 2017, 76, 21651–21663.
- 17. Buch, N., Velastin, S.A., Orwell, J. (2011) A review of computer vision techniques for the analysis of urban traffic. IEEE Trans. Intell. Transp. Syst. 2011, 12:920–939. DOI: 10.1109/TITS.2011.2119372.10.1109/TITS.2011.2119372
- 18. Daigavane, P.M., Bajaj, P.R. (2010) Real Time Vehicle Detection and Counting Method for Unsupervised Traffic Video on Highways. Int. J. Comput. Sci. Netw. Secur. 2010, 10:112–117.
- 19. Chen, S.C., Shyu, M.L., Zhang, C. (2001) An Intelligent Framework for Spatio-Temporal Vehicle Tracking. Proceedings of the 4th IEEE Intelligent Transportation Systems, Oakland, CA, USA. 25–29 August 2001.
- 20. Gupte, S., Masoud, O., Martin, R.F., Papanikolopoulos, N.P. (2002) Detection and Classification of Vehicles. IEEE Trans. Intell. Transp. Syst. 2002, 3:37–47. DOI: 10.1109/6979.994794.10.1109/6979.994794
- 21. Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z. (2018) Single-Shot Refinement Neural Network for Object Detection. arXiv, 2018, arXiv:1711.06897.10.1109/CVPR.2018.00442
- 22. Liu, Y., Wang, R., Shan, S., Chen, X. (2018) Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 18–22 June 2018.10.1109/CVPR.2018.00730
- 23. Zhou, P. (2018) Scale-Transferrable Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 18–22 June 2018.10.1109/CVPR.2018.00062
- 24. Li, S. (2018) 3D-DETNet: a Single Stage Video-Based Vehicle Detector. Computer Science: Computer Vision and Pattern Recognition. arXiv, 2018, arXiv:1801.01769.
- 25. Wang, X., Cheng, P., Liu, X., Uzochukwu. (2018) Focal loss sensitive detectors for vehicle surveillance. In: 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). Vol. 2018-May, pp. 1–5.
- 26. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P. (2017) Focal Loss for Dense Object Detection. In: Proceedings of the IEEE International Conference on Computer Vision 2017-Octob, pp. 2999–3007.10.1109/ICCV.2017.324
- 27. Hu, X., Xu, X., Xiao, Y., Chen, H., He, S., Qin, J., Heng, P. (2019) A Scale-Insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems, 20(3). Mar 2019, pp. 1010–1019.10.1109/TITS.2018.2838132
- 28. Gandhi, R. (2018) R-CNN, Fast R-CNN, Faster R-CNN, YOLO Object Detection Algorithms. July 9, 2018. https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e.
- 29. Basto, M, Pereira, J. (2012) An SPSS R-Menu for Ordinal Factor Analysis. Journal of Statistical Software, 46(4), pp. 1–29.