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Significant HOG-Histogram of Oriented Gradient Feature Selection for Human Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 132 - Number 17 |
| Year of Publication: 2015 |
| Authors: Muhammed Jamshed Alam Patwary, Shahnaj Parvin, Subrina Akter |
10.5120/ijca2015907704
|
Muhammed Jamshed Alam Patwary, Shahnaj Parvin, Subrina Akter . Significant HOG-Histogram of Oriented Gradient Feature Selection for Human Detection. International Journal of Computer Applications. 132, 17 ( December 2015), 20-24. DOI=10.5120/ijca2015907704
Abstract
Detecting human efficiently is an important field of research and has many applications such as intelligent vehicle, robotics and video surveillance. Histogram of Oriented Gradient (HOG) is one of the eminent algorithms for human shape detection. HOG features are extracted from all location of a dense grid on an image region and use linear Support Vector Machine (SVM) to classify the combined features. Although HOG gives an accurate description of the contour of human body, it requires a large computational time. We studied the fundamental idea and consider features that have high percentage to contain edge. In this proposed method we used difference of Gaussian to obtain the edge percentage of each feature. Then a threshold is used to remove features with low edge percentage. Selected features then classified using linear SVM. Experiments on INRIA dataset demonstrate that the proposed method not only reduce the dimension of the HOG features but also outperforms.
References
- Subra M., Karen D.:A novel Equation based classifier for detecting human in Images. In International Journal of Computer Applications (0975 – 8887) Volume 72– No.6, May 2013
- Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection.In:IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2005)
- Takuya K., Akinori H., and Takio K.: selection of Histogram of oriented gradients. In M. Ishikawa et al. (Eds.): ICONIP 2007, Part II, LNCS 4985, pp. 598–607, 2008. c Springer-Verlag Berlin Heidelberg 2008
- Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, Chichester (1998)
- C. Papageorgiou and T. Poggio. A trainable system for object detection. IJCV, 38(1):15–33, 2000
- A. Mohan, C. Papageorgiou, and T. Poggio. Example-based object detection in images by components. PAMI, 23(4):349–361, April 2001
- Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: Proc. of Computer Vision and Pattern Recognition (2003)
- R. Ronfard, C. Schmid, and B. Triggs. Learning to parse pictures of people. The 7th ECCV, Copenhagen, Denmark, volume IV, pages 700–714, 2002.
- P. Felzenszwalb and D. Huttenlocher. Efficient matching of pictorial structures. CVPR, Hilton Head Island, South Carolina, USA, pages 66–75, 2000.
- S. Ioffe and D. A. Forsyth. Probabilistic methods for finding people. IJCV, 43(1):45–68, 2001.
- Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Proc of the 9th International Conf. of Computer Vision, Nice, vol. 1, pp. 734–741(2003).
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