International Science Index


10007186

Moving Object Detection Using Histogram of Uniformly Oriented Gradient

Abstract:

Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

References:
[1] S. Zhang, C.Bauckhage, and A. B. Cremers. "Informed haar-like features improve pedestrian detection." Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
[2] P. Viola, and M. Jones. "Rapid object detection using a boosted cascade of simple features." Proc. of Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, 2001.
[3] F. Suaard, A. Rakotomarmonjy, A. Bensrhair, and A. Broggi, “Pedestrian detection using infrared images and histograms of oriented gradients,” Prof. of Intelligent Vehicles Symposium, June 2006.
[4] T. Watanabe, S. Ito, and K. Yokoi, “Co-occurrence histograms of oriented gradients for pedestrian detection,” Proc. of Pacific-Rim Symposium on Image and Video Technology, pp.37-47, 2009
[5] R. N. Hota, K. Jonna, and P. R. Krishna. "On-road vehicle detection by cascaded classifiers." Proc. of the Third Annual ACM Bangalore Conference. ACM, 2010.
[6] W. Zheng, and L.Liang. "Fast car detection using image strip features." In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. IIEEE, 2009.
[7] N. Dalal and B. Triggs. "Histograms of oriented gradients for human detection." In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, 2005.
[8] J. A. K. Suykens and J. Vandewalle, “Least Square Support Vector Machine Classifiers,” Neural Network Letter, vol. 9, pp.293-300, 1999.
[9] C. C. Chung, C. J. Lin, “LibSVM: A Library for Support Vector Machines,” 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm/ (access at 2016/4/20)
[10] C. Cortes and V. Vapnik, “Support vector networks,” Machine Learning, vol. 20, pp.273-297, 1995
[11] Q. Zhu, A. Avidan, M.-C. Yeh, and K.-T. Cheng, "Fast human detection using a cascade of histograms of oriented gradients." In Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). vol. 2, 2006.
[12] P. Luo, Y. Tian, X. Wang, and X. Tang "Switchable deep network for pedestrian detection." Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
[13] O. Deniz, G. Bueno, J. Salido, and F. D. la Torre, “Face recognition using histrogram of oriented gradients,” Pattern Recognition Letter, vol. 32, no. 12 pp.159-1602, 2011.
[14] C.-Y. Su and J. F. Yang, “Histogram of gradient phase: A new local descriptor for face recognition,” IET Computer Vision, vol. 8, no. 6, pp.556-567, December 2014.
[15] R. E. Osuna, R. Freund, and F. Girosit. "Training support vector machines: an application to face detection." In Proc. of IEEE Computer Vision and Pattern Recognition, 1997.
[16] V. Vapnik. The Nature of Statistical Learning Theory. New York Springer-Verlag, 1995.
[17] C. Papageorgiou and T. Poggio, "A trainable system for object detection." International Journal of Computer Vision, vol.38.1, pp.15-33, 2000.
[18] P. Viola, M. J. Jones, and D. Snow. "Detecting pedestrians using patterns of motion and appearance." International Journal of Computer Vision 63.2: 153-161, 2005.