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Robust Motion Detection and Tracking of Moving Objects using HOG Feature and Particle Filter
Bhuyain Mobarok Hossain, Stephen Karungaru, Kenji Tereda
Pages - 9 - 16     |    Revised - 31-12-2018     |    Published - 01-02-2019
Volume - 13   Issue - 1    |    Publication Date - February 2019  Table of Contents
Video Surveillance System, Robust Tracking, HOG feature, Particle Filter.
Detection and tracking of moving objects has gained significant importance due to intense technological progress in the field of computer science dealing with video surveillance systems. Human motion is generally nonlinear and non-Gaussian and thus many algorithms are not suitable for tracking. One of the applications to maintain universal security is crowd control. The main problem of video surveillance is continuous monitoring with regard to crime prevention. For security monitoring of live surveillance systems, target identification and tracking strategies can automatically send warnings to monitoring officers. In this paper, we propose a robust tracking of a specified person using the individuals' feature. The proposed method to determine automatic detection and tracking combines Histogram of Oriented Gradient (HOG) feature detection with a particle filter. The Histogram oriented Gradient features are applied to single detection window for the identification of human area, after we use particle filters for robust specific people tracking using color and skin color based on the characteristics of a target individual. We have been improving the implementation, evaluation system of our proposed methods. In our systems, for experiments, we choose structured crowded scenes. From our experimental results, we have achieved high accuracy detection rates and robust motion tracking for specific targets.
1 Google Scholar 
2 BibSonomy 
3 refSeek 
4 Doc Player 
5 Scribd 
6 SlideShare 
A. E. Dubois and A. Mitiche, "Real-time system for high-level video representation: application to video surveillance", Proc. of SPIE Int. Symp. on Electronic Imaging, Conf. on Visual Comm. and Image Proc. (VCIP), Santa Clara, USA, 5022 (2003), 530-541.
Aggarwal, J.K., Cai, Q., "Human motion analysis: a review" Computer Vision and Image Understanding ,Volume 73, Issue 3, 1 March 1999, Pages 428-440.
Bahera H. Nayef," A Comparison between Linear and Non-Linear Machine Learning Classifiers," Journal of Al-Nahrain University, Vol.19 (2), June, 2016, pp.145-153.
Bhuyain Mobarok Hossain, Stephen Karungaru, Kenji Terada, Akinori Tsuji,"Human Detection and Tracking Using HOG Feature and Particle Filter in Video Surveillance System," International Journal of Advanced Intelligence Volume 9, Number 3, pp.397-407, January, 2018.
Bhuyain Mobarok Hossain, Stephen Karungaru, Kenji Terada, Akinori Tsuji,"Real time Specified person Tracking in a Crowded Scene Using Particle Filter," The International Conference on Electrical Engineering (ICEE2017),July,4-7,2017 (Weihei, China).
Bristow and S. Lucey,"Why do linear svms trained on HOG features perform so well?," CoRR, vol. abs/1406.2419, 2014.
D. Reid, "An algorithm for tracking multiple targets," IEEE Trans. on Automation and Con-trol, Vol. AC-24, December 1979, p84-90.
Di Xie, Lu Dang, Ruofeng Tong," Video Based Head Detection and Tracking Surveillance System," 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012).
Haofu Liao, "Human detection based on Histograms Oriented Gradients and SVM". EECE-7373, Pattern Recognition, SPRING, April 23, 2013.
Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans Rev. 34(3),pp 334-352 , 2004.
Li, Teng, et al. "Crowded scene analysis: A survey." IEEE transactions on circuits and systems for video technology 25.3: 367-386, 2015.
M. M. Naushad Ali,M. Abdullah-Al-Wadud, et al," Moving Object Detection and Tracking using Particle Filter," Applied Mechanics and Materials," ISSN: 1662-7482, Vols. 321-324, pp 1200-1204,
M. Wegmuller, J. P. von der Weid, P. Oberson, and N. Gisin, "High resolution fiber distributed measurements with coherent OFDR," in Proc. ECOC'00, 2000, paper 11.3.4, p. 109.
Malik SOUDED et al," An Object Tracking in Particle Filtering and Data Association Framework, Using SIFT Features, "International Conference on Imaging for Crime Detection and Prevention(ICDP)(2011).
Mmohammed Lahraichi, Khalid Housni, Samir Mbarki," Visual Tracking Using Particle Filter Based on Gabor Features," International Journal of Intelligent Engineering & System, Vol.11, No.4, January 15, 2018.
N. Dalal and B. Triggs. "Histograms of orient-ed gradients for human detection". Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
Qi, Z., Ting, R. Husheng, F. Jinlin, Z.: "Particle filter object tracking based on Harris-SIFT feature matching". Proc. Eng. 29, 924? 929 (2012).
Ruiyue et al," Multiple human detection and tracking based on head detection for real-time video surveillance," Journa lMultimedia Tools and Applications archive Volume 74 Issue 3, Pages 729-742, February 2015.
S. Munder, C. Schnoerr, and D. M. Gavrila. Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models. IEEE Transactions Intelligent Transportation Systems, 9(2):333{343, 13, 2008.
Yizheng Cai, Robust Visual Tracking for Multiple Targets. B.E., Zhejiang University, 2003 ECCV: 9th European Conference on Computer Vision, Graz, May 7-13, 2006 proceedings, part IV (pp.107-118).
Mr. Bhuyain Mobarok Hossain
Faculty of Engineering / Systems Innovation Engineering Tokushima University Tokushima, 770-8506, Japan - Japan
Dr. Stephen Karungaru
Faculty of Engineering / Systems Innovation Engineering Tokushima University Tokushima, 770-8506, Japan - Japan
Dr. Kenji Tereda
Faculty of Engineering / Systems Innovation Engineering Tokushima University Tokushima, 770-8506, Japan - Japan