CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Vehicle Classification using Density based Multi-feature Approach in Support Vector Machine Classifier

by Pradeep Kumar Mishra, Biplab Banerjee
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 7
Year of Publication: 2013
Authors: Pradeep Kumar Mishra, Biplab Banerjee
10.5120/12367-8703

Pradeep Kumar Mishra, Biplab Banerjee . Vehicle Classification using Density based Multi-feature Approach in Support Vector Machine Classifier. International Journal of Computer Applications. 71, 7 ( June 2013), 1-6. DOI=10.5120/12367-8703

@article{ 10.5120/12367-8703,
author = { Pradeep Kumar Mishra, Biplab Banerjee },
title = { Vehicle Classification using Density based Multi-feature Approach in Support Vector Machine Classifier },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 7 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number7/12367-8703/ },
doi = { 10.5120/12367-8703 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:34:51.892430+05:30
%A Pradeep Kumar Mishra
%A Biplab Banerjee
%T Vehicle Classification using Density based Multi-feature Approach in Support Vector Machine Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 7
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A density based multi-feature combination approach to classify vehicles using support vector machine (SVM) classifier is proposed in this paper. Features like Haar, Gradient, RGB and Pyramidal histogram of oriented gradients are extracted from vehicle images and a kernel density approximation is performed. The results are compared with another set of multi-feature combination and effects of using kernel density estimators and various kernels like Gaussian, Polynomial and Histogram intersection in SVM are also studied. It is found that the above mentioned feature combination produces good results in comparison and effects of scaling, orientation and shadowing were properly compensated. A sample set of 9360 images were used with half of them used for training the classifier and the other half for testing. The vehicles were clustered into four categories, namely 2-wheeler, 3- wheeler, light motor vehicle (LMV) and heavy motor vehicles (HMV) and an accuracy greater than 90% has been observed.

References
  1. H. Bai, J. Wu, and C. Liu. Motion and haar-like features based vehicle detection. In Multi-Media Modelling Conference Proceedings, 2006 12th International, pages 4–pp. IEEE, 2006.
  2. Y. Bai, L. Guo, L. Jin, and Q. Huang. A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In Image Processing (ICIP), 2009 16th IEEE International Conference on, pages 3305–3308. IEEE, 2009.
  3. N. Buch, J. Orwell, and S. Velastin. 3d extended histogram of oriented gradients (3dhog) for classification of road users in urban scenes. 2009.
  4. N. W. Campbell, B. T. Thomas, and T. Troscianko. Automatic segmentation and classification of outdoor images using neural networks. International Journal of Neural Systems, 8(01):137–144, 1997.
  5. P. Gehler and S. Nowozin. On feature combination for multiclass object classification. In Computer Vision, 2009 IEEE 12th International Conference on, pages 221–228. IEEE, 2009.
  6. B. Han, D. Comaniciu, and L. Davis. Sequential kernel density approximation through mode propagation. In Proceeding of European Conference on Computer Vision, 2004.
  7. B. Han, D. Comaniciu, Y. Zhu, and L. Davis. Sequential kernel density approximation and its application to realtime visual tracking. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(7):1186–1197, 2008.
  8. A. Haselhoff and A. Kummert. A vehicle detection system based on haar and triangle features. In Intelligent Vehicles Symposium, 2009 IEEE, pages 261–266. IEEE, 2009.
  9. L. -L. Huang, A. Shimizu, Y. Hagihara, and H. Kobatake. Gradient feature extraction for classification-based face detection. Pattern Recognition, 36(11):2501–2511, 2003.
  10. O. Javed, K. Shafique, and M. Shah. A hierarchical approach to robust background subtraction using color and gradient information. In Motion and Video Computing, 2002. Proceedings. Workshop on, pages 22–27. IEEE, 2002.
  11. Y. -x. Jin, K. Zhang, J. T. Kwok, and H. -c. Zhou. Fast and accurate kernel density approximation using a divide-andconquer approach. Journal of Zhejiang University SCIENCE C, 11(9):677–689, 2010.
  12. M. Kafai and B. Bhanu. Dynamic bayesian networks for vehicle classification in video. Industrial Informatics, IEEE Transactions on, 8(1):100–109, 2012.
  13. G. Khairun Nor Aimi. Palm oil classification using RGB and fuzzy. PhD thesis, Universiti Malaysia Pahang, 2010.
  14. A. Klaser and M. Marszalek. A spatio-temporal descriptor based on 3d-gradients. 2008.
  15. X. Li, H. Fu, and J. Xu. Automatic vehicle classification based on video with bp neural networks. In Wireless Communications, Networking and Mobile Computing, 2008. WiCOM'08. 4th International Conference on, pages 1–3. IEEE, 2008.
  16. X. Liu and T. Yu. Gradient feature selection for online boosting. In Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pages 1–8. IEEE, 2007.
  17. S. Messelodi, C. M. Modena, and G. Cattoni. Vision-based bicycle/motorcycle classification. Pattern recognition letters, 28(13):1719–1726, 2007.
  18. N. R. Mudigonda, R. Rangayyan, and J. E. L. Desautels. Gradient and texture analysis for the classification of mammographic masses. Medical Imaging, IEEE Transactions on, 19(10):1032–1043, 2000.
  19. P. E. Rybski, D. Huber, D. D. Morris, and R. Hoffman. Visual classification of coarse vehicle orientation using histogram of oriented gradients features. In Intelligent Vehicles Symposium (IV), 2010 IEEE, pages 921–928. IEEE, 2010.
  20. Z. Sun, G. Bebis, and R. Miller. Quantized wavelet features and support vector machines for on-road vehicle detection. In Control, Automation, Robotics and Vision, 2002. ICARCV 2002. 7th International Conference on, volume 3, pages 1641–1646. IEEE, 2002.
  21. E. Tasnim and A. Ghani. Pineapple distribution classification using RGB and fuzzy. PhD thesis, Universiti Malaysia Pahang, 2010.
  22. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages I–511. IEEE, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Vehicle Classification

Powered by PhDFocusTM