Deep CNN Model for Condition Monitoring of Road Traffic: An Application Of Computer Vision
Main Article Content
Abstract
The traffic surveillance system accumulates an enormous amount of data regarding road traffic each second. Monitoring these data with the human eye is a tedious process and it also requires manpower for monitoring. Deep learning Convolutional Neural Network (DLCNN) can be utilized for traffic monitoring and control. The traffic surveillance data are preprocessed to construct the training dataset. The Traffic net is constructed by transferring the network to traffic applications and retraining it with a self-established data set. This Traffic-net can be used for regional detection in large scale applications. Further, it can be implemented across-the-board. Further, DLCNN is used for prediction of traffic status i.e., dense traffic, low traffic, accident, and fire occurred from test sample. Finally, the simulations revealed that the proposed DLCNN resulted in superior performance as compared to existing model.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
References
Bashar, A. (2019). “Survey onEvolving Deep Learning Neural Network Architectures”. Journal of
Artificial Intelligence, 1(02), 73-82.
Zoe Bartlett et al, “A Novel Online Dynamic Temporal Context Neural Network Framework for the
Prediction of Road Traffic Flow” IEEE Access, vol.7, 2019.
H. Lei et al., ``A deeply supervised residual network for HEp-2 cell classification via cross-modal transfer
learning,'' Pattern Recognit., vol. 79, pp. 290302, Jul. 2018.
P. Wang, L. Li, Y. Jin, and G. Wang, ``Detection of unwanted traffic congestion based on existing
surveillance system using in freeway via a CNNarchitecture trafficNet,'' in Proc. 13th IEEE Conf. Ind.
Electron. Appl., May/Jun. 2018, pp. 11341139.
X. Zhu, Y.Wang, J. Dai, L. Yuan, and Y.Wei, ``Flowguided feature aggregation for video object detection,''
in Proc. ICCV, Mar. 2017, pp. 408417.
Z. Zhao. Chen, X.Wu, P. C. Chen, and J. Liu, ``LSTM network: A deep learning approach for short-term
traffic forecast,'' IET Intell. Transp. Syst., vol. 11, no. 2, pp. 6875, Mar. 2017.
P. Li, D. Wang, L. Wang, and H. Lu, ``Deep visual tracking: Review and experimental comparison,'' Pattern
Recognit., vol. 76, pp. 323338, Apr. 2018.
P. Wang and J. Di, ``Deep learning-based object classification through multimode fiber via a
CNNarchitecture SpeckleNet,'' Appl. Opt., vol. 57, no. 28, pp. 82588263, 2018.
J. Zhao, Z. Zhang, W. Yu, and T.-K. Truong, ``A cascade coupled convolutional neural network guided
visual attention method for ship detection from SAR images,'' IEEE Access, vol. 6, pp. 5069350708, 2018.
T. Pamula, ``Road traffic conditions classification based on multilevel filtering of image content using
convolutional neural networks,'' IEEE Intel. Transp. Syst. Mag., vol. 10, no. 3, pp. 1121, Jun. 2018.