Hostname: page-component-89b8bd64d-shngb Total loading time: 0 Render date: 2026-05-07T22:15:41.235Z Has data issue: false hasContentIssue false
  • English
  • Français

ON-ROAD VEHICLE CLASSIFICATION BASED ON RANDOM NEURAL NETWORK AND BAG-OF-VISUAL WORDS

Published online by Cambridge University Press:  18 May 2016

Khaled F. Hussain  and
Ghada S. Moussa
Khaled F. Hussain
Affiliation:
Faculty of Computers and Information, Assiut University, Assiut, Egypt E-mail: khussain@aun.edu.eg
Ghada S. Moussa
Affiliation:
Civil Engineering Department, Assiut University, Assiut, Egypt E-mail: ghada.moussa@aun.edu.eg
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

A large increase in the number and types of vehicles occurred due to the growth in population. This fact brings the need for efficient vehicle classification systems that can be used in traffic surveillance and intelligent transportation systems. In this study, a multi-type vehicle classification system based on Random Neural Networks (RNNs) and Bag-Of-Visual Words (BOVWs) is developed. A 10-fold cross-validation technique is used, with a large dataset, to assess the proposed approach. Moreover, the BOVW–RNN's classification performance is compared with LIVCS, a vehicle classification system based on RNNs. The results reveal that BOVW–RNN classification system produces more reliable and accurate classification results than LIVCS. The main contribution of this paper is that the developed system can serve as a framework for many vehicle classification systems.

Information

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 30 , Issue 3: Erol Gelenbe's 70th Birthday , July 2016 , pp. 403 - 412
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2016

References

1.Abdelbaki, H.M., Hussain, K. & Gelenbe, E. (2001). A laser intensity image based automatic vehicle classification system. IEEE Intelligent Transportation Systems Conference Proceedings. Oakland, CA, USA: IEEE.Google Scholar
2.Atalay, V. & Gelenbe, E. (1992). Parallel algorithm for colour texture generation using the random neural network model. International Journal of Pattern Recognition and Artificial Intelligence 6: 437–446.Google Scholar
3.Bakircioglu, H., Gelenbe, E. & Kocak, T. (1997). Image enhancement and fusion with the random neural network. Turkish Journal of Electrical Engineering and Computer Sciences 5: 65–77.Google Scholar
4.Buch, N., Velastin, S.A. & Orwell, J. (2011). A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions On Intelligent Transportation Systems 12(3): 920–939.Google Scholar
5.Cho, H.J. & Tseng, M.T. (2013). A support vector machine approach to CMOS-based radar signal processing for vehicle classification and speed estimation. Mathematical and Computer Modelling 58(1): 438–448.Google Scholar
6.Cramer, C. & Gelenbe, E. (2000). Video quality and traffic QoS in learning-based subsampled and receiver-interpolated video sequences. IEEE Journal on Selected Areas in Communications 18: 150–167.Google Scholar
7.Cramer, C., Gelenbe, E. & Gelenbe, P. (1998). Image and video compression. IEEE Potentials 17: 29–33.Google Scholar
8.Csurka, G., Dance, C., Fan, L., Willamowski, J. & Bray, C. (2004). Visual categorization with bags of keypoints. Workshop on Statistical Learning in Computer Vision, ECCV 1(1–22): 1–2.Google Scholar
9.Dallalzadeh, E., Guru, D.S., Manjunath, S. & Suraj, M.G. (2012). Classification of moving vehicles in traffic videos. In Meghanathan, N., Chaki, N. & Nagamalai, D. (eds), Advances in Computer Science and Information Technology. Computer Science and Engineering. Berlin, Heidelberg: Springer, pp. 211–221.Google Scholar
10.De Sousa Matos, F.M. & de Souza, R.M.C.R. (2012). An image vehicle classification method based on edge and PCA applied to blocks. IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1688–1693.Google Scholar
11.Gelenbe, E. (1989). Random neural networks with negative and positive signals and product form solution. Neural Computation 1(4): 502–510, MIT Press.Google Scholar
12.Gelenbe, E. & Fourneau, J. (1999). Random neural networks with multiple classes of signals. Neural Computation 11(4): 953–963.Google Scholar
13.Gelenbe, E. & Hussain, K. (2002). Learning in the multiple class random neural network. IEEE Transactions on Neural Networks 13: 1257–1267.Google Scholar
14.Gelenbe, E., Hussain, K. & Abdelbaki, H. (2000). Random neural network texture model. Proceedings of the 5th Conference on Applications of Artificial NeuralNetworks in Image Processing, San Jose, CA, USA, 27–28 January. Bellingham, WA: SPIE, pp. 104–111.Google Scholar
15.Gelenbe, E., Hussain, K. & Kaptan, V. (2005). Simulating autonomous agents in augmented reality. Journal of Systems and Software 74: 255–268.Google Scholar
16.Gelenbe, E., Timotheou, S. & Nicholson, D. (2010). Random neural network for emergency management. Workshop on Grand Challenges in Modeling, Simulation and Analysis for Homeland Security, MSAHS, vol. 10.Google Scholar
17.Georgiopoulos, M., Li, C. & Kocak, T. (2011). Learning in the feed-forward random neural network: A critical review. Performance Evaluation, 68(4): 361–384.Google Scholar
18.Hussain, K. & Varol, K. (2004). Modeling and simulation with augmented reality. RAIRO-Operations Research 38(2): 89–103.Google Scholar
19.Hussain, K.F. & Moussa, G.S. (2005). Laser intensity vehicle classification system based on random neural network. Proceedings of the 43rd Annual Southeast Regional Conference, vol. 1, ACM.Google Scholar
20.Kafai, M. & Bhanu, B. (2012). Dynamic Bayesian networks for vehicle classification in video. IEEE Transactions On Industrial Informatics 8(1): 100–109.Google Scholar
21.Lin, W.H., Dahlgren, J. & Huo, H. (2003). An enhancement to speed estimation using single loop detectors. Proceeding of Intelligent Transportation Systems, vol. 1, pp. 417–422.Google Scholar
22.Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2): 91–110.Google Scholar
23.Lu, R. & Shen, Y. (2005). Image segmentation based on random neural network model and Gabor filters. Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, Shanghai, China, 1–4 September. Piscataway, NJ: IEEE, pp. 6464–6467.Google Scholar
24.Ma, X. & Grimson, W.E.L. (2005). Edge-based rich representation for vehicle classification. Tenth IEEE International Conference on Computer Vision. ICCV 2005, vol. 2, pp. 1185–1192.Google Scholar
25.Moussa, G.S. (2014). Vehicle type classification with geometric and appearance attributes. International Journal of Civil, Architectural Science and Engineering 8(3): 273–278.Google Scholar
26.Ng, J.Y. & Tay, Y.H. (2012). Image-based vehicle classification system. arXiv preprint arXiv, the 11th Asia-Pacific ITS Forum and Exhibition, Kaoshiung, Taiwan. pp. 1204–2114.Google Scholar
27.Sivic, J. & Zisserman, A. (2008). Efficient visual search for objects in videos. Proceedings of the IEEE 96(4): 548–566.Google Scholar
28.Tian, B., Morris, B.T., Tang, M., Liu, Y., Yao, Y., Gou, C. & Tang, S. (2014). Hierarchical and networked vehicle surveillance in ITS: a survey. IEEE Transactions on Intelligent Transportation Systems 16(2): 557–580.Google Scholar
29.Timotheou, S. (2010). The random neural network: a survey. The Computer Journal 53(3): 251–267.Google Scholar
30.Yousaf, K., Iftikhar, A. & Javed, A. (2012). Comparative analysis of automatic vehicle classification techniques: a survey. International Journal of Image, Graphics and Signal Processing (IJIGSP), vol. 9, pp. 52–59.Google Scholar
31.Zhang, B. (2013). Reliable classification of vehicle types based on cascade classifier ensembles. IEEE Transactions on Intelligent Transportation Systems 14(1): 322–332.Google Scholar
32.Zhang, G., Avery, R.P. & Wang, Y. (2007). Video-based vehicle detection and classification system for real-time traffic data collection using uncalibrated video cameras. Transportation Research Record: Journal of the Transportation Research Board 1993(1): 138–147.Google Scholar
33.Zhang, L., Li, S.Z., Yuan, X. & Xiang, S. (2007). Real-time object classification in video surveillance based on appearance learning. IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07, pp. 1–8.Google Scholar