Abstract
Nowadays, the traffic situation is very complicated in Vietnam. Traffic jams happen frequently in densely populated or peak hours. So, it is necessary for an automatic warning system to police officers about traffic status in time and effectively. In this research, the system automatically estimates the vehicle motion rate and the number of vehicles. This system is useful for roads with lots of hard-to-distinguish traffic. The method is based on computer vision such as background subtraction and deep learning such as the CNN network model and the CenterNet object detection model. Experimental data are taken from videos in Vietnam with the view in front of the Kien Giang and Da Nang hospitals. The achieved classification model results can predict with 91.7% accuracy on the test set, the precision of crowded road predictions is 81.9% precision and 70.4% recall. When the CenterNet model is applied to estimate the number of vehicles, the model reached 1.261 MAE. The speed of the system when it is run experimentally on hardware using Nvidia Geforce GTX 1070 GPU reached 4.6 FPS.
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References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Chan, A.B., Vasconcelos, N.: Counting people with low-level features and Bayesian regression. IEEE Trans. Image Process. 21(4), 2160–2177 (2012)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. arXiv preprint, arXiv:1512.00567v3, Dec 2015
Huynh, C.K., Dang, T.K., Nguyen, C.A.: Motorbike counting in heavily crowded scenes, future data and security engineering. In: 8th International Conference, FDSE 2021, pp. 175–194 (2021)
Darwishalzughaibi, A., Ahmed Hakami, H., Chaczko, Z.: Review of human motion detection based on background subtraction techniques. Int. J. Comput. Appl. 122, 1–5 (2015)
Guo, J., et al.: A new moving object detection method based on frame difference and background subtraction. In: Iop Conference, Dec, pp. 242–246. ASME Press (2017)
Gui-Ping, B., Yi-Lin, Q.: An adaptive edge-detection method based on Canny algorithm. Electr. Des. Eng. 116, 886–893 (2017)
Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. arXiv preprint, arXiv:1808.01244v2 (2018)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, realtime object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2016. https://doi.org/10.1109/CVPR.2016.91
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR 2015. arXiv preprint, arXvir:1409.1556v5
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: CenterNet: keypoint triplets for object detection. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6568–6577. https://doi.org/10.1109/ICCV.2019.00667
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual learning for image recognition, Dec 2015. arXiv preprint, arXiv:1512.03385v1
Law, H., Deng, J.: CornerNet: detecting objects as paired key points. Int. J. Comput. Vision 128(3), 642–656 (2019). https://doi.org/10.1007/s11263-019-01204-1
Liu, W., et al.: SSD: Single Shot MultiBox Detector, vol. 9905, pp. 21–37. https://doi.org/10.1007/978-3-319-46448-0_2 (2016)
Asmaa, O., Mokhtar, K., Abdelaziz, O.: Road traffic density estimation using microscopic and macroscopic parameter. Image Vis. Comput. 31, 887–894 (2013)
Phuong, H., Thu, K.: Estimating the traffic density from traffic cameras. In: 8th International Conference, FDSE 2021, pp. 248–263
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580–587. doi: https://doi.org/10.1109/CVPR.2014.81
Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440–1448. https://doi.org/10.1109/ICCV.2015.169
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Machine Intell. 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient based learning applied to document recognition (1998)
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. arXiv preprint, arXiv:1904.07850v2 (2019)
Zang, Y., et al: Vehicles detection in complex urban traffic and counting using linear quadratic estimation technique. In: Int. Conf. on Inventive Systems and Control, Valencia, Spain, pp. 603–607 (2018)
Acknowledgment
The authors would like to thank Vinh Thanh Van ward police department in RachGia city (Kien Giang, VietNam) for providing the traffic videos. We would also like to express our gratitude to the police for their help in collecting and publishing the datasets.
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Phuong, V.L.Q., Dong, N.V., Thu, T.N.M., Khang, P.N. (2022). Combine Clasification Algorithm and Centernet Model to Predict Trafic Density. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_40
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