Vehicle Classification and Counting for Vehicle Census
Keywords:
Vehicle Census, Vehicle Classification, Image Processing,Abstract
Vehicle classification has been significantly important to vehicle census as it provides traffic count information to reflect the traffic density of a particular roadway. However, it has been a time consuming and sophisticated task to classify different vehicles into the desired category. Besides, the hardware-based technique used for classification leads to high cost of implementation and maintenance. Thus, we proposed an image processing based solution to extract the features of each vehicle in the traffic scene. The proposed framework incorporates a combination of detection, tracking and classification of vehicle to ensure high accuracy and performance for vehicle census. Experimental results show that our proposed framework can be applicable in real world applications.References
P. Piyush, R. Rajan, L. Mary, and B. I. Koshy, “Vehicle detection and classification using audio-visual cues,” In Signal Processing and Integrated Networks (SPIN), 2016 3rd International Conference on, pp. 726-730, 2016. IEEE.
K. Yousaf, A. Iftikhar, and A. Javed, “Comparative analysis of automatic vehicle classification techniques: a survey,” International Journal of Image, Graphics and Signal Processing, vol. 4, no. 9, p. 52, 2012.
S. Harsha and C. Sandeep, “Real Time Traffic Density And Vehicle Count Using Image Processing Technique,” IJRCCT, vol. 4, no. 8, pp. 594-598, 2015.
R. P. Avery, Y. Wang and G. S. Rutherford, “Length-based vehicle classification using images from uncalibrated video cameras,” In Intelligent Transportation Systems, 2004. Proceedings. The 7th International IEEE Conference on, pp. 737-742, 2004. IEEE.
S. A. Daramola and A. Ademola, “Vehicle Classification Algorithm using Size and Shape,” International Journal of Soft Computing and Engineering, vol. 6, no. 1, pp. 78-81, 2016.
N. V. Hung, N. H. Dung, T. M. Hoang, and N. T. Dzung, “Vehicle classification by estimation of the direction angle in a mixed traffic flow,” In Communications and Electronics (ICCE), 2016 IEEE Sixth International Conference on, pp. 365-368, 2016. IEEE.
Y. Benezeth, D. Sidibé and J. B. Thomas, “Background subtraction with multispectral video sequences,” In IEEE International Conference on Robotics and Automation workshop on Non-classical Cameras, Camera Networks and Omnidirectional Vision (OMNIVIS), pp. 6-p, 2014.
R. K. Kota and T. C. Rao, “Analysis Of Classification And Tracking In Vehicles Using Shape Based Features,” International Journal of Innovative Research and Development, 2278–0211, vol. 2, no. 8, 2013.
S. Ojha and S. Sakhare, “Image processing techniques for object tracking in video surveillance-a survey,” In Pervasive Computing (ICPC), 2015 International Conference on, pp. 1-6, 2015. IEEE.
J. Tabak, Geometry: the language of space and form. Infobase Publishing, 2014.
G. S. Moussa, “Vehicle type classification with geometric and appearance attributes,” World Academy of Science, Engineering and Technology, International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, vol. 8, no. 3, pp. 277-282, 2014.
R. A. Hadi, G. Sulong, and L. E. George, “Vehicle detection and tracking techniques: a concise review,” arXiv preprint arXiv:1410.5894, 2014.
M. A. Farooque and J. S. Rohankar “Survey on various noises and techniques for denoising the color image,” International Journal of Application or Innovation in Engineering & Management (IJAIEM), vol. 2, no. 11, pp. 217-221, 2013.
S. Kaur and S. Kaur, “An efficient approach for number plate extraction from vehicles image under image processing,” International Journal of Computer Science and Information Technologies, vol. 5, no. 3, pp. 2954-2959, 2014.
C. N. Van and C. N. Ngoc, “Vehicle Classification in Video Based on Shape Analysis,” In Modelling Symposium (EMS), 2014 European, pp. 151-157, Oct. 2014. IEEE.
Y. Cao, D. Stuart, W. Ren and Z. Meng, “Distributed containment control for multiple autonomous vehicles with double-integrator dynamics: algorithms and experiments,” IEEE Transactions on Control Systems Technology, vol. 19, no. 4, pp. 929-938, 2011.
Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.
