Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Object Tracking using MACH filter and Optical Flow in Cluttered Scenes and Variable Lighting Conditions

Authors: Waqar Shahid Qureshi, Abu-Baqar Nisar Alvi

Abstract:

Vision based tracking problem is solved through a combination of optical flow, MACH filter and log r-θ mapping. Optical flow is used for detecting regions of movement in video frames acquired under variable lighting conditions. The region of movement is segmented and then searched for the target. A template is used for target recognition on the segmented regions for detecting the region of interest. The template is trained offline on a sequence of target images that are created using the MACH filter and log r-θ mapping. The template is applied on areas of movement in successive frames and strong correlation is seen for in-class targets. Correlation peaks above a certain threshold indicate the presence of target and the target is tracked over successive frames.

Keywords: Correlation filters, optical flow, log r-θ mapping.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329933

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1780

References:


[1] C. F. Hester and D. Casasent, "Multivariant technique for multiclass pattern recognition", Applied Optics, Vol. 19, 1758-1761 (1980).
[2] A. Mahalanobis, B.V.K. Vijaya Kumar, S. Song, S.R.F. Sims, J.F. Epperson, "Unconstrained correlation filters", Applied Optics, Vol. 33, pp. 3751-3759, 1994.
[3] A. Mahalanobis, B.V.K. Vijaya Kumar, S.R.F. Sims, "Distance-classifier correlation filters for multiclass object recognition", Applied Optics, Vol. 35, pp. 3127-3133, 1996.
[4] P. Bone, R. Young and C. R. Chatwin, "Position, rotation, scale and orientation invariant multiple object recognition from cluttered scenes", Optical Engineering, Vol. 45, pp. 077203-1to -8, 2006.
[5] A. Mahalanobis, A. Forman, M. Bower, N. Day, and R. F. Cherry, "A quadratic distance classifier for multi-class SAR ATR using correlation filters," in Ultrahigh Resolution Radar, R. S. Vickers, ed. Proc. SPIE 1875, 84-95, 1993.
[6] A. Mahalanobis, D. W. Carlson, and B. V. K. Vijaya Kumar,"Evaluation of MACH and DCCF correlation filters for SAR ATR using MSTAR public data base," in Algorithms for Synthetic Aperture Radar Imagery V, E. G. Zelnio, ed. Proc. SPIE 3370, 460-469, 1998.
[7] I. Kypraios, R. C. D. Young, P. Birch, C. Chatwin, "Object recognition within cluttered scenes employing a Hybrid Optical Neural Network (HONN) filter", Optical Engineering, Special Issue on Trends in Pattern Recognition, Vol. 43, No. 8, 1839-1850, 2004.
[8] Ph. Refregier, "Optimal trade-off filters for noise robustness, sharpness of the correlation peak and Horner efficiency", Optics Letters, Vol. 16, No. 11, 829-831, 1991.
[9] D. Young, "Straight lines and circles in the log polar image", Proc. Of British Machine Vision conference, 2000.
[10] B.D. Lucas, T. Kanade, "An iterative image registration technique with an application to stereo vision", Proceedings of Imaging understanding workshop, pp 121ÔÇö130, 1981.