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http://dx.doi.org/10.9717/kmms.2018.21.5.558

People Detection Algorithm in the Beach  

Choi, Yu Jung (Dept. of Computer and Communication Eng., Kangwon National University)
Kim, Yoon (Dept. of Computer and Communication Eng., Kangwon National University)
Publication Information
Journal of Korea Multimedia Society / v.21, no.5, 2018 , pp. 558-570 More about this Journal
Abstract
Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.
Keywords
Object Detection; Object Tracking; GMM; Background Cut; SVM; KCF Tracker;
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  • Reference
1 R. Jain, "Dynamic Scene Analysis Using Pixel-Based Processes," Computer , Vol. 14, No. 8, pp. 12-18, 1981.   DOI
2 Z. Zivkovic, "Improved Adaptive Gaussian Mixture Model for Background Subtraction," IEEE Proceedings of the 17th International Conference on Pattern Recognition, Vol. 2, pp. 28-31, 2004.
3 J. Sun, W. Zhang, X. Tang, and H.Y. Shum, "Background Cut," Proceeding of European Conference on Computer Vision, pp. 628-641, 2006.
4 I. Leichter, M. Lindenbaum, and E. Rivlin, “Mean Shift Tracking with Multiple Reference Color Histograms,” Computer Vision and Image Understanding, Vol. 144, No. 3, pp. 400-408, 2010.
5 S. Zhou, R. Chellappa, and B. Moghaddam, “Visual Tracking and Recognition Using Appearance-Adaptive Models in Particle Filter,” IEEE Transactions on Image Processing, Vol. 13, No. 11, pp. 1491-1506, 2004.   DOI
6 C.J. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121-167, 1998.   DOI
7 J.F. Henriques, R. Caseiro, P. Martins, and J. Batista, "High-Speed Tracking with Kernelized Correlation Filters," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 3, pp. 583-596, 2015.   DOI
8 N. Dalal and T. Bill, "Histograms of Oriented Gradients for Human Detection," Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 886-893, 2005.
9 Q. Zhu, M.C. Yeh, K.T. Cheng, and S. Avidan, "Fast Human Detection Using a Cascade of Histograms of Oriented Ggradients," Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 1491-1498, 2006.
10 G.G. Lee, S.H. Song, K.H. Ka, J.Y. Yoon, J.J. Kim, and W.Y. Kim, "Adaptive Background Modeling for Crowded Scenes," Journal of Korea Multimedia Society, Vol. 11, No. 5, pp. 597-609, 2008.
11 N.J.B. McFarlane and C.P. Schofield, "Segmentation and Tracking of Piglets in Images," Machine Vision and Applications, Vol. 8, No. 3, pp. 187-193, 1995.   DOI