Road Traffic Density Classification to Improvise Traffic System Using Convolutional Neural Network (CNN)-Reference-Cited by-同舟云学术

Road Traffic Density Classification to Improvise Traffic System Using Convolutional Neural Network (CNN)

Published:2023 Issue: Volume: Page:397-404
ISSN:2367-3370
Container-title:Proceedings of International Conference on Recent Trends in Computing
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Author:

Singh Nidhi,Kumar Manoj

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-8825-7_34

Reference11 articles.

1. Nguyen LAT, Ha TX (2022) A novel approach of traffic density estimation using CNNs and computer vision. European J Electr Eng Comput Sci 5(4):80–84

2. Pang CCC, Lam WWL, Yung NHC (2007) A method for vehicle count in the presence of multiple-vehicle occlusions in traffic images. IEEE Trans Intell Transp Syst 8(3):441–459

3. Kapse SA, Bhoyar RA, Dhokne CN (2016) Classification of traffic density using three class neural network classifiers. Int J Eng Res Technol (IJERT), ISSN, 2278–0181

4. Nubert J, Truong NG, Lim A, Tanujaya HI, Lim L, Vu MA (2018) Traffic density estimation using a convolutional neural network. arXiv preprint arXiv:1809.01564

5. Shipu MK, Mamun FA, Razu SH, Nishat Sultana M (2022) TrafficNN: CNN-based road traffic conditions classification. In: Soft computing for security applications, pp 241–253, Springer, Singapore

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