An Efficient Deep Learning Based Hyperbolic Back Propagate Boltzmann Neural Network for Automated Vehicular Surveillance



Automated Vehicular Surveillance, Deep Learning, Gaussian mac clustering approach, Metaheuristic chaos vortex optimization, Hyperbolic back propagate Boltzmann neural network


In recent years, there has been a significant expansion in the infrastructure for video surveillance, which has resulted in an increase in the number of intelligent surveillance systems that use computer vision and pattern recognition algorithms. In this article, we offer a unique intelligent surveillance system that is based on deep learning and is utilized for the management of road vehicles. In other words, the system is able to label any vehicle via the use of computer vision, and as a result, it is able to quickly distinguish cars that have visual tags. This capability allows the system to extract the vehicle visual tags that are present on urban roadways. The visual tags that are discussed in this article include the license plate number, the color of the car, and the kind of vehicle. These visual tags also contain a variety of other attributes, such as passing location and passing time. In this work  the particular area video footage was retrieved. Then the formation of clustered hub was done for the fusion of the several footage data . The violation based vehicle details are grouped by using the Gaussian mac clustering approach. Then from the grouped features the specialized features according to the required event was isolated using the Metaheuristic chaos vortex optimization. Finally the proposed hyperbolic back propagate boltzmann neural network (HBPBNN) architecture detects the vehicle and its related violations  in a precise manner. The whole experimentation was carried out in a real time database. On-road experimental findings show that the algorithm outperforms the most cutting-edge vehicle recognition algorithm in testing data sets. The findings of the comparative assessment indicated that the recommended model performed better than the other models in use.


Download data is not yet available.


S. A. Najeeb, R. H. Raza, A. Yusuf, and Z. Sultan, "Fine-grained vehicle classification in urban traffic scenes using deep learning," in Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications, 2022, pp. 902-908.

H. Gupta and O. P. Verma, "Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach," Multimedia Tools and Applications, vol. 81, pp. 19683-19703, 2022.

A. Kherraki and R. El Ouazzani, "Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring," IAES International Journal of Artificial Intelligence, vol. 11, p. 110, 2022.

A. Aboah, "A vision-based system for traffic anomaly detection using deep learning and decision trees," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 4207-4212.

P. Jagannathan, S. Rajkumar, J. Frnda, P. B. Divakarachari, and P. Subramani, "Moving vehicle detection and classification using gaussian mixture model and ensemble deep learning technique," Wireless Communications and Mobile Computing, vol. 2021, 2021.

W. Liu, Z. Luo, and S. Li, "Improving deep ensemble vehicle classification by using selected adversarial samples," Knowledge-Based Systems, vol. 160, pp. 167-175, 2018.

H. Fu, H. Ma, Y. Liu, and D. Lu, "A vehicle classification system based on hierarchical multi-SVMs in crowded traffic scenes," Neurocomputing, vol. 211, pp. 182-190, 2016.

A. Şentaş, İ. Tashiev, F. Küçükayvaz, S. Kul, S. Eken, A. Sayar, et al., "Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification," Evolutionary Intelligence, vol. 13, pp. 83-91, 2020.

X. Wang, W. Zhang, X. Wu, L. Xiao, Y. Qian, and Z. Fang, "Real-time vehicle type classification with deep convolutional neural networks," Journal of Real-Time Image Processing, vol. 16, pp. 5-14, 2019.

L. Zhuo, L. Jiang, Z. Zhu, J. Li, J. Zhang, and H. Long, "Vehicle classification for large-scale traffic surveillance videos using convolutional neural networks," Machine Vision and Applications, vol. 28, pp. 793-802, 2017.

V. Murugan and V. Vijaykumar, "Automatic moving vehicle detection and classification based on artificial neural fuzzy inference system," Wireless Personal Communications, vol. 100, pp. 745-766, 2018.

Z. Dong, Y. Wu, M. Pei, and Y. Jia, "Vehicle type classification using a semisupervised convolutional neural network," IEEE transactions on intelligent transportation systems, vol. 16, pp. 2247-2256, 2015.

M. A. Hedeya, A. H. Eid, and R. F. Abdel-Kader, "A super-learner ensemble of deep networks for vehicle-type classification," IEEE Access, vol. 8, pp. 98266-98280, 2020.

F. C. Soon, H. Y. Khaw, J. H. Chuah, and J. Kanesan, "Semisupervised PCA convolutional network for vehicle type classification," IEEE Transactions on Vehicular Technology, vol. 69, pp. 8267-8277, 2020.

S. Awang, N. M. A. N. Azmi, and M. A. Rahman, "Vehicle type classification using an enhanced sparse-filtered convolutional neural network with layer-skipping strategy," IEEE Access, vol. 8, pp. 14265-14277, 2020.

N. Nasaruddin, K. Muchtar, and A. Afdhal, "A lightweight moving vehicle classification system through attention-based method and deep learning," IEEE Access, vol. 7, pp. 157564-157573, 2019.

Schematic representation of the suggested methodology




How to Cite

Meenakshi, N., Juvanna, I., Anbhazhagan, P., & Shanmuganathan, T. (2023). An Efficient Deep Learning Based Hyperbolic Back Propagate Boltzmann Neural Network for Automated Vehicular Surveillance. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 232–244. Retrieved from



Research Article

Similar Articles

You may also start an advanced similarity search for this article.