Storage Optimization using Adaptive Thresholding Motion Detection
Published online first on March 9, 2021.
Data storage is always an issue, especially for video data from CCTV cameras that require huge amounts of storage. Moreover, monitoring past events is a laborious task. This paper proposes a motion detection method that requires fewer calculations and reduces the required data storage up to 70%, as it stores only the informative frames, enabling the security personnel to retrieve the required information more quickly. The proposed method utilized a histogram-based adaptive threshold for motion detection, and therefore it can work in variable luminance conditions. The proposed method can be applied to streamed frames of any CCTV camera to efficiently store and retrieve informative frames.
S. Khan and D. Lee, "Efficient deinterlacing method using simple edge slope tracing," Optical Engineering, vol. 54, no. 10, Oct. 2015, Art. no. 103108. https://doi.org/10.1117/1.OE.54.10.103108
S. Khan, D. Lee, M. A. Khan, A. R. Gilal, and G. Mujtaba, "Efficient Edge-Based Image Interpolation Method Using Neighboring Slope Information," IEEE Access, vol. 7, pp. 133539-133548, 2019. https://doi.org/10.1109/ACCESS.2019.2942004
S. Khan et al., "Image Interpolation via Gradient Correlation-Based Edge Direction Estimation," Scientific Programming, vol. 2020, Apr. 2020, Art. no. 5763837. https://doi.org/10.1155/2020/5763837
S. Arora, K. Bhatia, and V. Amit, "Storage optimization of video surveillance from CCTV camera," in 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Oct. 2016, pp. 710-713. https://doi.org/10.1109/NGCT.2016.7877503
M. Hazas, J. Morley, O. Bates, and A. Friday, "Are there limits to growth in data traffic? on time use, data generation and speed," in Proceedings of the Second Workshop on Computing within Limits, Irvine, CA, USA, Jun. 2016, pp. 1-5. https://doi.org/10.1145/2926676.2926690
M. H. Padgavankar, "Big Data Storage and Challenges," International Journal of Computer Science and Information Technologies, vol. 5, no. 2, pp. 2218-2223, Apr. 2014.
J. M. Caplan, L. W. Kennedy, and G. Petrossian, "Police-monitored CCTV cameras in Newark, NJ: A quasi-experimental test of crime deterrence," Journal of Experimental Criminology, vol. 7, no. 3, pp. 255-274, Sep. 2011. https://doi.org/10.1007/s11292-011-9125-9
F. N. Tawfeeq, "Real Time Motion Detection in Surveillance Camera Using MATLAB," International Journal of Advanced Research inComputer Science and Software Engineering, vol. 3, no. 9, pp. 622-626, Sep. 2013.
M. Rushambwa, T. Chamunorwa, and K. Nyachionjeka, "Real Time Wireless Surveillance System with Motion Detection and Device Control over Internet," International Journal of Scientific and Research Publications, vol. 6, no. 3, Mar. 2016, Art. no. 470.
S. 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, Jan. 2011. https://doi.org/10.1109/TCSVT.2010.2087812
A. Upasana, B. Manisha, G. Mohini, and K. Pradnya, "Real Time Security System using Human Motion Detection," International Journal of Computer Science and Mobile Computing, vol. 4, no. 11, pp. 245-250, 2015.
J. C. M. Butil, M. L. F. Magsisi, J. H. Pua, P. K. Se, and R. Sagum, "The Application of Genetic Algorithm in Motion Detection for Data Storage Optimization," International Journal of Computer and Communication Engineering, vol. 3, no. 3, pp. 199-202, May 2014. https://doi.org/10.7763/IJCCE.2014.V3.319
C. Saravanan, "Color Image to Grayscale Image Conversion," in 2010 Second International Conference on Computer Engineering and Applications, Mar. 2010, vol. 2, pp. 196-199. https://doi.org/10.1109/ICCEA.2010.192
M. V. Sarode and P. R. Deshmukh, "Image Sequence Denoising with Motion Estimation in Color Image Sequences," Engineering, Technology & Applied Science Research, vol. 1, no. 6, pp. 139-143, Dec. 2011. https://doi.org/10.48084/etasr.54
A. B. Altamimi and H. Ullah, "Panic Detection in Crowded Scenes," Engineering, Technology & Applied Science Research, vol. 10, no. 2, pp. 5412-5418, Apr. 2020. https://doi.org/10.48084/etasr.3347
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