Abstract
Performance of face detection system drops drastically when blur effect is present in the surveillance video. Motivated by this problem, the proposed method deblurs facial images to detect and improve faces degraded by blur in the scenario like banks, ATMs where sparse crowd is present. Prevalent Viola Jones technique detect faces, but fails in the presence of blur. Hence, to overcome this, first the target frame is decomposed using Discrete Wavelet Transform(DWT) into LL, LH, HL and HH bands. The LL band is processed using Lucy-Richardson’s algorithm which removes blur using Point Spread Function (PSF). Then the super enhanced de-blurred frame without ripples is given into Viola-Jones algorithm. It has been observed and validated experimentally that, the detection rate in the Viola Jones algorithm has been improved by 47 %. Experimental results illustrate the effectiveness of the proposed algorithm.
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Acknowledgements
This work has been supported under DST Fast Track Young Scientist Scheme for the project entitled, Intelligent Video Surveillance System for Crowd Density Estimation and Human Abnormal Analysis, with reference no. SR/FTP/ETA-49/2012. Also, it has been supported by UGC under Major Research Project Scheme entitled, Intelligent Video Surveillance System for Human Action Analysis with reference F.No.41-592/2012(SR).
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Menaka, K., Yogameena, B., Nagananthini, C. (2017). Improving Face Detection in Blurred Videos for Surveillance Applications. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-2107-7_14
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DOI: https://doi.org/10.1007/978-981-10-2107-7_14
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