Tracking and Size Estimation of Objects in Motion based on Median of Localized Thresholding
Keywords:Filtering, Object segmentation, Machine vision, Motion estimation, Localized thresholding, Bilateral filtering,
Motion detection and tracking play an important role in Computer vision and Robotics. Optical flow based methods to estimate the motion are widely explored during the last decade. The motion information retrieved from these techniques has enormous applications. Video analysis based on the size, speed, and directions of objects have wider applications in computer vision, robotics and watermarking. Segmentation of moving objects based on the optical flow is very challenging. In this paper, we present a model to estimate the size of a moving object based on the optical flow technique and present localized thresholding technique. Over segmentation is reduced by the proposed local thresholding technique and use of bilateral filtering. We compare our results with Sagar et al. scheme.
Sagar Gujjunoori, S. Sai Satyanarayana Reddy, Gouthaman K V. .Tracking and Size Estimation of Objects in Motion Published in: Machine Vision and Information Technology (CMVIT). International Conference on Date of Conference: 17-19 Feb. 2017 Date Added to IEEE Xplore: 16 March 2017 ISBN Information:INSPEC Accession Number: 16757954 DOI: 10.1109/CMVIT.2017.8 Publisher: IEEE.
 D. M. Gavrila,The Visual Analysis of Human Movement: A Survey, Computer Vision and Image Understanding, vol. 73, pages: 82-98, 1999.
 AbdullaAl-Kaff, David Martn, FernandoGarca, Arturo de la Escalera, JosMara Armingol, Survey of computer vision algorithms and applications for unmanned aerial vehicles, Expert Systems with Applications Volume 92, February 2018, Pages 447-463.
 L. P. Yaroslavsky, Digital Picture Processing. An Introduction. Springer Verlag, 1985.
 V. Aurich and J.Weule, Non-linear gaussian filters performing edge preserving diffusion, in Proceedings of the DAGM Symposium, pp. 538545, 1995.
 S. M. Smith and J. M. Brady, SUSAN A new approach to low level image processing, International Journal of Computer Vision, vol. 23, no. 1,pp. 4578, May 1997.
 B.K.P. Horn and B.G. Schunck, Determining optical flow.Artificial Intelligence, vol. 17, pp. 185-203, 1981.
 Bruce D. Lucas and Takeo Kanade, An iterative image registration technique with an application to stereo vision. In Proceedings of the 7th international joint conference on Artificial intelligence - (IJCAIâ€™81), Vol. 2. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 674-679.
 Gunnar Farneback, Two-frame motion estimation based on polynomial expansion. In Proceedings of the 13th Scandinavian conference on Image analysis (SCIAâ€™03), Josef Bigun and Tomas Gustavsson (Eds.). Springer-Verlag, Berlin, Heidelberg, pp. 363-370.
 N. Goyette, P.-M. Jodoin, F. Porikli, J. Konrad, and P.Ishwar, changedetection.net: A new change detection benchmark dataset, in Proc. IEEE Workshop on Change Detection (CDW-2012) at CVPR-2012, Providence, RI, 16-21 Jun.,2012