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

Moving Object Detection Scheme for Automated Video Surveillance Systems

Full Text (PDF, 566KB), PP.49-58

Views:82   Downloads:4


Sanjay Singh, Sumeet Saurav, Chandra Shekhar, Anil Vohra

Index Terms

Moving Object Detection;Automated Video Surveillance System;Smart Camera System


In every automated video surveillance system, moving object detection is an important pre-processing step leading to the extraction of useful information regarding moving objects present in a video scene. Most of the moving object detection algorithms require large memory space for storage of background related information which makes their implementation a difficult task on embedded platforms which are typically constrained by limited resources. Therefore, in order to overcome this limitation, in this paper we present a memory optimized moving object detection scheme for automated video surveillance systems with an objective to facilitate its implementation on standalone embedded platforms. The presented scheme is a modified version of the original clustering-based moving object detection algorithm and has been coded using C/C++ in the Microsoft Visual Studio IDE. The moving object detection results of the proposed memory efficient scheme were qualitatively and quantitatively analyzed and compared with the original clustering-based moving object detection algorithm. The experimental results revealed that there is 58.33% reduction in memory requirements in case of the presented memory efficient moving object detection scheme for storing background related information without any loss in accuracy and robustness as compared to the original clustering based scheme.

Cite This Paper

Sanjay Singh, Sumeet Saurav, Chandra Shekhar, Anil Vohra,"Moving Object Detection Scheme for Automated Video Surveillance Systems", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.7, pp.49-58, 2016.DOI: 10.5815/ijigsp.2016.07.06


[1]R.J. Radke, S. Andra, O.A. Kofahi, and B. Roysam, Image Change Detection Algorithms: A Systematic Survey, IEEE Transactions on Image Processing, Vol. 14, No. 3, pp. 294-307, 2005.

[2]L. Lacassagne, A. Manzanera, J. Denoulet, and A. Merigot, High performance motion detection: some trends toward new embedded architectures for vision systems, Journal of Real-Time Image Processing, Vol. 4, No. 2, pp. 127-146, 2009.

[3]L. Bruzzone and D.F. Prieto, Automatic Analysis of the Difference Image for Unsupervised Change Detection, IEEE Transaction on Geosciences and Remote Sensing, Vol. 38, No. 3, pp. 1171–1182, 2000.

[4]J.E. Colwell and F.P. Weber, Forest Change Detection, In Proceedings: 15th International Symposium on Remote Sensing of the Environment, pp. 839-852, 1981.

[5]W.A. Malila, Change Vector Analysis: An Approach for Detecting Forest Changes with Landsat, In Proceedings: Symposium on Machine Processing of Remotely Sensed Data, pp. 326-336, 1980.

[6]A. Singh, Review Article: Digital Change Detection Techniques using Remotely-sensed Data, International Journal of Remote Sensing, Vol. 10, No. 6, pp. 989-1003, 1989. 

[7]L.D. Stefano, S. Mattoccia, and M. Mola, A Change-detection Algorithm based on Structure and Color, In Proceedings: IEEE Conference on Advanced Video and Signal-Based Surveillance, pp. 252-259, 2003.

[8]Y.Z. Hsu, H.H. Nagel, and G. Rekers, New Likelihood Test Methods for Change Detection in Image Sequences, Computer Vision, Graphics, Image Processing, Vol. 26, No. 1, pp. 73-106, 1984.

[9]K. Skifstad and R. Jain, Illumination Independent Change Detection for Real World Image Sequences, Computer Vision, Graphics, Image Processing, Vol. 46, No. 3, pp. 387-399, 1989.

[10]A.S. Elfishawy, S.B. Kesler, and A.S. Abutaleb, Adaptive Algorithms for Change Detection in Image Sequence, Signal Processing, Vol. 23, No. 2, pp. 179-191, 1991.

[11]Z.S. Jain and Y.A. Chau, Optimum Multisensor Data Fusion for Image Change Detection, IEEE Transaction on System, Man and Cybernetics, Vol. 25, No. 9, pp. 1340-1347, 1995.

[12]K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, Wallflower: Principles and Practice of Background Maintenance, in Proceedings: Seventh International Conference on Computer Vision, pp. 255-261, 1999.

[13]C. Clifton, Change Detection in Overhead Imagery using Neural Networks, Applied Intelligence, Vol. 18, pp. 215-234, 2003.

[14]E. Durucan and T. Ebrahimi, Change Detection and Background Extraction by Linear Algebra, In Proceedings: IEEE, Vol. 89, No. 10, pp. 1368-1381, 2001.

[15]L. Li and M.K.H. Leung, Integrating Intensity and Texture Differences for Robust Change Detection, IEEE Transaction on Image Processing, Vol. 11, No. 2, pp. 105-112, 2002.

[16]S.C. Liu, C.W. Fu, and S. Chang, Statistical Change Detection with Moments under Time-Varying Illumination," IEEE Transaction on Image Processing, Vol. 7, No. 9, pp. 1258-1268, 1998.

[17]A. Cavallaro and T. Ebrahimi, Video Object Extraction based on Adaptive Background and Statistical Change Detection, In Proceedings: SPIE Visual Communications and Image Processing, pp. 465-475, 2001.

[18]S. Huwer and H. Niemann, Adaptive Change Detection for Real-Time Surveillance Applications, In Proceedings: Third IEEE International Workshop on Visual Surveillance, pp. 37-46, 2000.

[19]T. Kanade, R.T. Collins, A.J. Lipton, P. Burt, and L. Wixson, Advances in Cooperative Multi-Sensor Video Surveillance, In Proceedings: DARPA Image Understanding Workshop, pp. 3-24, 1998.

[20]C. Stauffer and W.E.L. Grimson, Learning Patterns of Activity using Real-Time Tracking, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 747-757, 2000.

[21]D.E. Butler, V.M. Bove, and S. Sridharan, Real-Time Adaptive Foreground/Background Segmentation, EURASIP Journal on Applied Signal Processing, Vol. 2005, pp. 2292-2304, 2005.

[22]E.R. Chutani and S. Chaudhury, Video Trans-Coding in Smart Camera for Ubiquitous Multimedia Environment, In Proceedings: International Symposium on Ubiquitous Multimedia Computing, pp.185–189, 2008.

[23]M. Genovese and E. Napoli, ASIC and FPGA Implementation of the Gaussian Mixture Model Algorithm for Real-time Segmentation of High Definition Video, IEEE Transactions on Very Large Scale Integration, Vol. 22, No. 3, pp. 537-547, 2014.

[24]R. Rodriguez-Gomez, E.J. Fernandez-Sanchez, J. Diaz, and E. Ros, FPGA Implementation for Real-Time Background Subtraction Based on Horprasert Model, Sensors, Vol. 12, No. 1, pp. 585–611, 2012.