Smart Maneuvering of Security Camera

International Journal of Electronics and Communication Engineering

© 2022 by SSRG - IJECE Journal

Volume 9 Issue 5

Year of Publication : 2022

Authors : Dhatreyee Eluri, A. Raghu Ram

10.14445/23488549/IJECE-V9I5P103

How to Cite?

Dhatreyee Eluri, A. Raghu Ram, "Smart Maneuvering of Security Camera," SSRG International Journal of Electronics and Communication Engineering, vol. 9,  no. 5, pp. 17-20, 2022. Crossref, https://doi.org/10.14445/23488549/IJECE-V9I5P103

Abstract:

One of the ongoing demanding research problems in computer vision is visual surveillance in dynamic situations, particularly for humans and automobiles. It is a critical technology in the battle against terrorist attacks, crime, public health and safety, and effective traffic management. The endeavour entails the creation of an effective visual surveillance system for use in complex contexts. Detecting directional movement from a video is critical for target tracking, object categorization, activity recognition, and behaviour understanding in video surveillance. The first relevant phase of data is detecting object tracking in streaming video, and background subtraction is a typical method for foreground segmentation. Different backdrop subtraction approaches are simulated in this work to tackle the issues of lighting variance, background clutter, shadows, and concealment.

Keywords:

Computer vision, Motion detection, Background subtraction.

References:

[1] M. Cross, Guardian, Explainer: CCTV, 2009. [Online]. Available: http://www.theguardian.com/commentisfree/libertycentral/2009/nov/06/explainer-cctv-surveillance-cameras.
[2] E. C. Hannah, “Method and Apparatus for Processing Digital Video Camera Signals,” US Patent, pp. 192, 1996.
[3] J. A. Kalomiros, and J. Lygouras, “Design and Evaluation of a Hardware / Software FPGA-Based System for Fast Image Processing,” Microprocessors and Microsystems, vol. 32, no. 2, pp. 95-106, 2007. Crossref, https://doi.org/10.1016/j.micpro.2007.09.001
[4] D. Tian, “Utilizing a Smart Camera System for Immersive Telepresence,” US Patent Application 2014/0253667 A1, 2014.
[5] Berkeley Design Technology, Inc. Choosing a Processor: Benchmark and Beyond, World Wide Web, 2006. [Online]. Available: http://www.bdti.com/articles/20060301_TIDC_Choosing.pdf.
[6] D. Chun, “CCTV System Having Improved Detection Function and Detecting Method Suited for the System,” US Patent 5 671 009, 1997.
[7] Alan J. Lipton, Hironobu Fujiyoshi, and Raju S, Patil, “Moving Target Classification and Tracking from Real-Time Video,” In Proceedings of the 4th IEEE Workshop on Applications of Computer Vision, WACV ’98, IEEE Computer Society, vol. 8, 1998. Crossref, https://doi.org/10.1109/ACV.1998.732851
[8] Forsyth D.A, and Ponce J, “Computer Vision: A Modern Approach,” Pearson Education, Upper Saddle River, NJ, 2003.
[9] Ying-Li Tian, and Arun Hampapur, “Robust Salient Motion Detection with Complex Background for Real-Time Video Surveillance,” 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05), vol. 1, 2005. Crossref, https://doi.org/10.1109/ACVMOT.2005.106
[10] Shih-Chia Huang, “An Advanced Motion Detection Algorithm with Video Quality Analysis for Video Surveillance Systems,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 1, pp. 1-14, 2011. Crossref, https://doi.org/10.1109/TCSVT.2010.2087812
[11] Ahmed Elgammal, David Harwood, and Larry Davis, “Non-Parametric Model for Background Subtraction,” European Conference on Computer Vision, vol. 1843, pp. 751-767, 2000. Crossref, https://doi.org/10.1007/3-540-45053-X_48
[12] K Toyama et al., “Wallflower: Principles and Practice of Background Maintenance,” Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, no. 3, pp. 255-261, 1999. Crossref, https://doi.org/10.1109/ICCV.1999.791228
[13] L. Maddalena, and A. Petrosino, “A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications,” IEEE Transactions on Image Processing, vol. 17, no. 7, pp. 1168-1177, 2008. Crossref, https://doi.org/10.1109/TIP.2008.924285
[14] Geetha D et al., “Human and Animal Movement Detection in Agricultural Fields,” SSRG International Journal of Computer Science and Engineering, vol. 6, no. 1, pp. 15-18, 2019. Crossref, https://doi.org/10.14445/23488387/IJCSE-V6I1P103
[15] Lina J. Karam, and David Rice, Image Convolution Concepts and Applications Online Tutorial, Arizona State University. [Online]. Available: http://www.eas.asu.edu/~karam/2dconvolution/
[16] Akshay Choudhari et al., “Camera Surveillance System Using Motion Detection and Tracking,” International Journal of Innovative Research in Advanced Engineering (IJIRAE), KBTCOE, vol. 1, no. 4, pp. 38, 2014.
[17] Nishu Singla, “Motion Detection Based on Frame Difference Method,” International Journal of Information and Computation Technology, vol. 4, no. 15, pp. 1559-1565, 2014.
[18] Ponce J, and Forsyth D.A, “Vision of the Computer: A Modern Approach,” Pearson Education, Upper Saddle River, NJ, 2003.
[19] Steven W. Smith, “The Scientist and Engineer’s Guide to Digital Signal Processing,” Second Edition, California Technical Publishing, 1999.
[20] Nobuyuki Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979. Crossref, https://doi.org/10.1109/TSMC.1979.4310076
[21] Jain R, Kasturi R, and Schunck G, “Machine Vision,” McGraw Hill, 1995.