Case 6:22-cv-00466-ADA-DTG Document 54-4 Filed 01/04/23 Page 1 of 3
`Case 6:22-cv-00466-ADA-DTG Document 54-4 Filed 01/04/23 Page 1 of 3
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`EXHIBIT 20
`EXHIBIT 20
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`Case 6:22-cv-00466-ADA-DTG Document 54-4 Filed 01/04/23 Page 2 of 3
`ARTICLE IN PRESS
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`Signal Processing: Image Communication 20 (2005) 255–264
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`www.elsevier.com/locate/image
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`Moving object detection in wavelet compressed video$
`B. Ugur To¨ reyina, , A. Enis C- etina, Anil Aksaya, M. Bilgay Akhanb
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`aDepartment of Electrical and Electronics Engineering, Bilkent University, TR-06800 Bilkent, Ankara, Turkey
`bVisioprime, 30 St. Johns Rd., St. Johns, Woking, Surrey, GU21 7SA, UK
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`Received 3 June 2004; accepted 8 December 2004
`
`Abstract
`
`In many surveillance systems the video is stored in wavelet compressed form. In this paper, an algorithm for moving
`object and region detection in video which is compressed using a wavelet transform (WT) is developed. The algorithm
`estimates the WT of the background scene from the WTs of the past image frames of the video. The WT of the current
`image is compared with the WT of the background and the moving objects are determined from the difference. The
`algorithm does not perform inverse WT to obtain the actual pixels of the current image nor the estimated background.
`This leads to a computationally efficient method and a system compared to the existing motion estimation methods.
`r 2005 Published by Elsevier B.V.
`
`Keywords: Moving region detection; Wavelet compressed video
`
`1. Introduction
`
`the video is
`In many surveillance systems,
`compressed intra-frame only without performing
`motion compensated prediction due to legal
`reasons. Courts do not accept predicted image
`frames as legal evidence in many countries [5]. As a
`
`$B.U. To¨ reyin and A.E. Cetin’s work is partially funded by
`EU NoE MUSCLE and Turkish Academy of Sciences,
`TUBA–GEBIB.
` Corresponding author. Tel.: +90 312 290 1477;
`fax: +90 312 266 4192.
`E-mail addresses: ugur@ee.bilkent.edu.tr (B.U. To¨ reyin),
`cetin@ee.bilkent.edu.tr (A.E. C- etin), anil@ee.bilkent.edu.tr
`(A. Aksay), bilgay.akhan@visioprime.com (M.B. Akhan).
`
`0923-5965/$ - see front matter r 2005 Published by Elsevier B.V.
`doi:10.1016/j.image.2004.12.002
`
`result, a typical surveillance video is composed of a
`series of individually compressed image frames.
`In addition, many practical systems have built-in
`VLSI hardware image compressors directly storing
`the compressed video data coming from several
`cameras into a hard-disc. The main reason for this
`is that standard buses used in PC’s cannot handle
`the raw multi-channel video data. In this paper, it
`is assumed that the video data is available in
`wavelet compressed format. In many multi-chan-
`nel real-time systems,
`it is not possible to use
`uncompressed video due to available bus and
`processor limitations. The proposed moving object
`detection algorithm compares the wavelet trans-
`form (WT) of the current image with the WTs of
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`

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`Case 6:22-cv-00466-ADA-DTG Document 54-4 Filed 01/04/23 Page 3 of 3
`ARTICLE IN PRESS
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`264
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`B.U. To¨reyin et al. / Signal Processing: Image Communication 20 (2005) 255–264
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`of 16 video channels into the PCI bus of an
`ordinary PC in real-time. However, compressed
`video data of 16 channels can be handled by an
`ordinary PC and its buses, hence real-time motion
`detection can be implemented by the proposed
`algorithm.
`
`References
`
`[1] M. Antonini, M. Barlaud, P. Mathieu, I. Daubechies,
`Image coding using wavelet transforms, IEEE Trans.
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`[2] Aware Inc., 40 Middlesex Turnpike, Bedford, Massachu-
`setts, 01730, URL:www.aware.com, MotionWaveletst
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`[3] M. Bagci, Y. Yardimci, A.E. Cetin, Moving object
`detection using adaptive subband decomposition and
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`[4] R.T. Collins, A.J. Lipton, T. Kanade, H. Fujiyoshi, D.
`Duggins, Y. Tsin, D. Tolliver, N. Enomoto, O. Hasegawa,
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`RI-TR-00-12, Carnegie Mellon University, May 1998.
`[5] G.L. Foresti, P. Mahonen, C.S. Regazzoni, Multimedia
`Video-Based Surveillance Systems: Requirements, Issues
`and Solutions, Kluwer, Dordrecht, 2000.
`[6] I. Haritaoglu, D. Harwood, L. Davis, W4: who, when,
`where, what: a real time system for detecting and tracking
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`in: Third Face and Gesture Recognition Con-
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`[7] J.-C. Huang, W.-S. Hsieh, Wavelet-based moving object
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`[8] S. Naoi, H. Egawa, M. Shiohara, Image processing
`apparatus, U.S. Patent 6,141,435, October 31st, 2000.
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`in (un)compressed domains, IEEE Trans. Multimedia
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`IEEE Computer Society Conference on Computer Vision
`and Pattern Recognition, vol. 2, 1999.
`[11] Y. Taniguchi, Moving object detection apparatus and
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`[12] Visioprime Ltd., 30 St. Johns Road, St. Johns, Woking,
`Surrey, GU21 7SA, URL:www.visioprime.com.
`[13] R. Zaibi, A.E. Cetin, Y. Yardimci, Small moving object
`detection in video sequences,
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`
`Fig. 6. Detection result of the subband domain method using
`all of the 3rd level wavelet coefficients. Walking man marked as
`MAN1 is pointed.
`
`coefficients. Because, 4th level wavelet coefficients
`are obtained after four consecutive down-sampling
`steps, and a 16  16 object reduces to a 1  1
`object. In our software implementation, we ignore
`isolated coefficients to eliminate noise. Therefore,
`the method works up to the 4th level subband
`decomposition.
`
`5. Conclusion
`
`We developed a method for detecting motion in
`wavelet compressed video using only subband
`domain data without performing inverse wavelet
`transform. Our results assure us that the motion
`detection performance of
`the wavelet domain
`method is almost the same as methods using
`actual pixel data for motion detection. This is an
`expected result because subband domain data
`contains all the necessary information to recon-
`struct the actual image.
`The main advantage of the proposed method
`compared to regular methods is that it is not only
`computationally efficient but also it solves the
`bandwidth problem associated with video proces-
`sing systems. It is impossible to feed the pixel data
`
`