HOG based object detection and classification

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The intension of the project is to classify objects in real world and to tracks them throughout their life spans. Object detection algorithms use feature extraction and learning algorithms to classification of an object category. Our algorithm uses a combination of “histogram of oriented gradient” (HOG) and “support vector machine” (SVM) classifier to classify of objects. Results have shown this to be a robust method in both classifying the objects along with tracking them in real time world.



  • Keywords

    Object Detection; Object Classification; “Histogram of Oriented Gradient (HOG)”; “Support Vector Machine” (SVM).

  • References

      [1] DALAL, N. – TRIGGS, B. Histograms of oriented gradients for human detection. In Computer Vision and Pat- tern Recognition, 2005.CVPR 2005.IEEE Computer Society Conference on, volume 1, pages 886 –893 vol. 1, June 2005.

      [2] LOWE, D. G. Distinctive Image Features from Scale- Invariant Keypoints. International Journal Computer Vi- sion. November 2004, 60, 2, pages 91–110. ISSN 0920- 5691.

      [3] VIOLA, P. – JONES, M. J. Robust Real-Time Face Detection. International Journal of Computer Vision. May 2004, 57, 2, pages 137–154.ISSN 0920-5691.

      [4] N. Cristianini, J. Shawe-Taylor. An Introduction to Sup- port Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000. https://doi.org/10.1017/CBO9780511801389.

      [5] Histogram of Oriented Gradient, Pattern Recognition Systems – Lab

      [6] W. Shao, W. Yang, G. Liu, and L. J., ―Car detection from high-resolution aerial imagery using multiple feature IGARSS, 2012, pp. 4379–4382.

      [7] C. Shan, S. Gong, and P. W. McOwan. Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, 27:803–816, 2009. https://doi.org/10.1016/j.imavis.2008.08.005.

      [8] G. hao and M. Pietik ainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transaction on Pattern Analysis and Machine Intelligence, 29:915–928, 2007.

      [9] T. Kanade, J. F. Cohn, and Y. Tian. Comprehensive database for facial expression analysis. In Fourth IEEE International Conference on Automatic face and Gesture Recognition (FG’00), pages 46–53, 2000.

      [10] Zinal K Naik and Monali R Gandhi. A Review of Object Detection Based on Convolutional Neural Network. Proceedings of the 36th Chinese Control Conference July 26-28, 2017.

      [11] Rowley, H. A., Baluja, S. and Kanade, T.: Neural Net- works Based Face Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1):2238, 199.




Article ID: 15585
DOI: 10.14419/ijet.v7i3.3.15585

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.