INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.8, No.7, Jul. 2016

Motion Segmentation from Surveillance Video using modified Hotelling's T-Square Statistics

Full Text (PDF, 850KB), PP.41-48


Views:54   Downloads:0

Author(s)

Chandrajit M, Girisha R, Vasudev T

Index Terms

Motion segmentation;Video surveillance;Spatio-temporal;Hotelling's T-Square test

Abstract

Motion segmentation is an important task in video surveillance and in many high-level vision applications. This paper proposes two generic methods for motion segmentation from surveillance video sequences captured from different kinds of sensors like aerial, Pan Tilt and Zoom (PTZ), thermal and night vision. Motion segmentation is achieved by employing Hotelling's T-Square test on the spatial neighborhood RGB color intensity values of each pixel in two successive temporal frames. Further, a modified version of Hotelling's T-Square test is also proposed to achieve motion segmentation. On comparison with Hotelling's T-Square test, the result obtained by the modified formula is better with respect to computational time and quality of the output. Experiments along with the qualitative and quantitative comparison with existing method have been carried out on the standard IEEE PETS (2006, 2009 and 2013) and IEEE Change Detection (2014) dataset to demonstrate the efficacy of the proposed method in the dynamic environment and the results obtained are encouraging.

Cite This Paper

Chandrajit M, Girisha R, Vasudev T,"Motion Segmentation from Surveillance Video using modified Hotelling's T-Square Statistics", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.7, pp.41-48, 2016.DOI: 10.5815/ijigsp.2016.07.05

Reference

[1]J. Ahn, C. Choi, S. Kwak, K. Kim, and H. Byun. Human tracking and silhouette extraction for human robot interaction systems. Pattern Analysis and Applications, 12(2):167–177, 2009.

[2]N. Armanfard, M. Komeili, and E. Kabir. Ted: A texture-edge descriptor for pedestrian detection in video sequences. Pattern Recognition, 45(3):983 – 992, 2012.

[3]M. Chandrajit, R. Girisha, and T. Vasudev. Motion segmentation from surveillance videos using t-test statistics. In ACM India Computing Conference (COMPUTE '14). ACM, New York, 2014.

[4]M. Chandrajit, R. Girisha, and T. Vasudev. Motion segmentation from surveillance video sequences using chi-square statistics. In Emerging Research in Computing, Information, Communication and Applications, volume 2, pages 365–372. Elsevier, 2014.

[5]C. L. ChaoYang Lee, ShouJen Lin and C. Yang. An efficient continuous tracking system in real-time surveillance application. Journal of Network and Computer Applications, 35(3):1067 – 1073, 2012. Special Issue on Trusted Computing and Communications.

[6]F. Cheng and Y. Chen. Real time multiple objects tracking and identification based on discrete wavelet transform. Pattern Recognition, 39(6):1126 – 1139, 2006.

[7]Y. Chen, C. Chen, C. Huang, and Y. Hung. Efficient hierarchical method for background subtraction. Pattern Recognition, 40(10):2706 – 2715, 2007.

[8]E. Dallalazadeh and D. S. Guru. Moving vehicles extraction in traffic videos. IJMI, 3(4):236–240, 2011.

[9]S. Denman, C. Fookes, and S. Sridharan. Group segmentation during object tracking using optical flow discontinuities. In Image and Video Technology (PSIVT), 2010 Fourth Pacific-Rim Symposium on, pages 270–275, Nov 2010.

[10]A. Elgammal, R. Duraiswami, D. Harwood, and L. Davis. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE, 90(7):1151–1163, Jul 2002.

[11]R. Girisha. Some New Methodologies to Track Humans in a Single Environment using Single and Multiple Cameras-Doctoral Thesis. University of Mysore, Mysore, 2010.

[12]N. Goyette, P. Jodoin, F. Porikli, J. Konrad, and P. Ishwar. Changedetection.net: A new change detection benchmark dataset. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 1–8, June 2012.

[13]R. A. Johnson and D. W. Winchern. Applied Multivariate Statistical Analysis. Pearson Prentice Hall, 2007.

[14]H. J and S. O. A real-time system for monitoring of cyclists and pedestrians. In Visual Surveillance, 1999. Second IEEE Workshop on, (VS 99), pages 74–81, Jul 1999.

[15]T. Lim, B. Han, and J. H. Han. Modeling and segmentation of floating foreground and background in videos. Pattern Recognition, 45(4):1696 – 1706, 2012.

[16]C. Liu, P. C. Yuen, and G. Qiu. Object motion detection using information theoretic spatio temporal saliency. Pattern Recognition, 42(11):2897 – 2906, 2009.

[17]M. T. Lopez, A. F. Caballero, M. A. Fernandez, J. Mira, and A. E. Delgado. Visual surveillance by dynamic visual attention method. Pattern Recognition, 39(11):2194 – 2211, 2006.

[18]X. L. L Zappella and J. Salvi. New Trends in Motion Segmentation. In Pattern Analysis and Applications,InTech, 2009.

[19]L. Maddalena and A. Petrosino. The sobs algorithm: What are the limits? In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 21–26, June 2012.

[20]M. M. Mohamed Sedky and C. C. Chibelushi. Spectral-360: A physics-based technique for change detection. in proc of IEEE Workshop on Change Detection, 2014.

[21]R. S. Munoz. A bayesian plan-view map based approach for multiple-person detection and tracking. Pattern Recognition, 41(12):3665 – 3676, 2008.

[22]www.cvg.rdg.ac.uk/PETS2006/data.html [17 March 2016].

[23]www.cvg.rdg.ac.uk/PETS2009/data.html [17 March 2016].

[24]www.cvg.rdg.ac.uk/PETS2013/a.html [17 March 2016].

[25]A. Prati, R. Cucchiara, I. Mikic, and M. Trivedi. Analysis and detection of shadows in video streams: a comparative evaluation. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 2, pages II–571–II–576 vol.2, 2001.

[26]A. C. Rencher. Methods of multivariate analysis. Wiley-Interscience publication, 2001.

[27]G. S. K. P. Rui Wang, Filiz Bunyak. Static and moving object detection using flux tensor with split gaussian models. in proc of IEEE Workshop on Change Detection, 2014.

[28]A. Sadaf, J. Ali, M. Irfan,"Human Identification On the basis of Gaits Using Time Efficient Feature Extraction and Temporal Median Background Subtraction", IJIGSP, vol.6, no.3, pp.35-42, 2014. DOI: 10.5815/ijigsp.2014.03.05

[29]L. G. Shapiro and S. G C. Computer Vision. Prentice Hall, 2001.

[30]C. Stauffer and Grimson. Adaptive background mixture models for real-time tracking. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., volume 2, 1999.

[31]B. Subudhi, S. Ghosh, and A. Ghosh. Change detection for moving object segmentation with robust background construction under wronskian framework. Machine Vision and Applications, 24(4):795–809, 2013.

[32]A. T. S Johnsen. Real-time object tracking and classification using a static camera. In IEEE ICRA, 2009.

[33]M. S and G. R. Segmentation of motion objects from surveillance video sequences using temporal differencing combined with multiple correlation. In Advanced Video and Signal Based Surveillance, 2009. AVSS '09. Sixth IEEE International Conference on, pages 472–477, Sept 2009.

[34]H. Wang and D. Suter. A consensus-based method for tracking: Modelling background scenario and foreground appearance. Pattern Recognition, 40(3):1091 – 1105, 2007.

[35]Wang and Xiaogang. Intelligent multi-camera video surveillance: A review. Pattern Recogn. Lett., 34(1):3–19, Jan 2013.

[36]L. W. Weiming Hu, Tieniu Tan and M. S. A survey on visual surveillance of object motion and behaviors. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 34(3):334–352, Aug 2004.

[37]J. Yang, B. Price, X. Shen, Z. Lin and J. Yuan, "Fast Appearance Modeling for Automatic Primary Video Object Segmentation," in IEEE Transactions on Image Processing, vol. 25, no. 2, pp. 503-515, Feb. 2016.

[38]A. Yilmaz, O. Javed, and M. Shah. Object tracking: A survey. ACM Comput. Surv., 38(4), dec 2006.

[39]D. Zhang and G. Lu. Segmentation of moving objects in image sequence: A review. Circuits, Systems and Signal Processing, 20(2):143–183, 2001.

[40]S. Zhang, D. A. Klein, C. Bauckhage and A. B. Cremers, "Fast moving pedestrian detection based on motion segmentation and new motion features", Multimedia Tools and Applications, pp 1-20, Springer Science, 2015.