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Abstract A computer vision problem is considered. The prototype is the optical flow car tracker within MATLAB Computer Vision System Toolbox, tracking cars in one-direction road traffic. For adapting the tracker to work with other problems of moving cars stationarycameradetection, having different properties (video length, resolution, velocity of those cars, camera disposition, prospect), it is parametrized. Altogether there are 19 parameters in the created MATLAB function, fulfilling the tracking. Eight of them are influential regarding the tracking results. Thus, these influential parameters are ranked into a nonstrict order by the testing-experience-based criterion, where other videos are used. The preference means that the parameter shall be varied above all the rest to the right side of the ranking order. The scope of the developed MATLAB tool is unbounded when objects of interest move near-perpendicularly and camera is stationary. For cases when camera is vibrating or unfixed, the parametrized tracker can fit itself if vibrations are not wide. Under those restrictions, the tracker is effective for visual statistical surveillance of one-direction road traffic. References Parker J. R. Algorithms for Image Processing and Computer Vision / J. R. Parker. – Indianapolis : Wiley, 2011. – 480 p. 2. Klette R. Concise Computer Vision. An Introduction into Theory and Algorithms / R. Klette. – London : Springer, 2014. – 429 p. 3. Forsyth D. A. Computer Vision. A Modern Approach / D. A. Forsyth, J. Ponce. – New Jersey : Pearson, 2012. – 761 p. 4. Sonka M. Image Processing, Analysis, and Machine Vision / M. Sonka, V. Hlavac, R. Boyle. – Toronto : Thomson, 2008. – 829 p. 5. Mohr J. A computer vision system for rapid search inspired by surface-based attention mechanisms from human perception / J. Mohr, J.-H. Park, K. Obermayer // Neural Networks. – 2014. – Vol. 60. – P. 182–193. DOI: http://x.doi.org/10.1016/j.neunet.2014.08.010 6. Cyganek B. Hybrid computer vision system for drivers’ eye recognition and fatigue monitoring / B. Cyganek, S. Gruszczyсski // Neurocomputing. – 2014. – Vol. 126. – P. 78 – 94. DOI: http:/ /dx.doi.org/10.1016/j.neucom.2013.01.048 7. Park M.-W. Construction worker detection in video frames for initializing vision trackers / M.-W. Park, I. Brilakis // Automation in Construction. – 2012. – Vol. 28. – P. 15–25. DOI: http:// dx.doi.org/10.1016/j.autcon.2012.06.001 8. Balasubramanian A. Utilization of Robust Video Processing Techniques to Aid Efficient Object Detection and Tracking / A. Balasubramanian, S. Kamate, N. Yilmazer // Procedia Computer Science. – 2014. – Vol. 36. – P. 579 – 586. DOI: http://dx.doi.org/10.1016/j.procs.2014.09.057 9. Bhattacharyya S. High-speed target tracking by fuzzy hostilityinduced segmentation of optical flow field / S. Bhattacharyya, U. Maulik, P. Dutta // Applied Soft Computing. – 2009. – Vol. 9, Issue 1. – P. 126–134. DOI: http://dx.doi.org/10.1016/j.asoc.2008.03.012 10. Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration / [S. Ji, X. Fan, D. W. Roberts, A. Hartov, K. D. Paulsen] // Medical Image Analysis. – 2014. – Vol. 18, Issue 7. – P. 1169–1183. DOI:http://dx.doi.org/10.1016/j.media.2014.07.001 11. A self-adaptive optical flow method for the moving object detection in the video sequences / [Y. Xin, J. Hou, L. Dong, L. Ding] // Optik – International Journal for Light and Electron Optics. – 2014. – Vol. 125, Issue 19. – P. 5690–5694. DOI:http://dx.doi.org/10.1016/j.ijleo.2014.06.092 12. Xiong J.-Y. An Improved Optical Flow Method for Image Registration with Large-scale Movements / J.-Y. Xiong, Y.-P. Luo, G.-R. Tang // Acta Automatica Sinica. – 2008. – Vol. 34, Issue 7. – P. 760 – 764. DOI: http://dx.doi.org/10.3724/ SP.J.1004.2008.00760

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