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Exploiting Regression Line and Doyle’s Distance to Track the Object via Color Optical Flow

M. H. Sidram, Nagappa U. Bhajantri


In the need of elite information of object, optical flow with gray attempts to emulate moderate essence. Since long time the color optical flow approach had been neglected. In this work, the contemporary policy has been put forward which endeavors to split out the optical flow for each channels such as R, G and B. The same is attempted through the well known optical flow schemes namely Horn-Schunk with BFB (Barron,Fleet,Beauchemin) kernel and Lukas-Kanade with Sobel gradient. Consequently, the obtained end results are merged to acquire the enhanced values of the motion vectors and in turn to isolate the moving objects from the static background. To overcome the drifting of template, the distance measures like histogram regression lines and Doyle’s strategy have suitably been extended in the work. The histogram regression lines of template and non-stationary objects are estimated to determine the matching between them and hence best match candidate template is replaced as new template. On the other hand, Doyle’s distance between the candidate templates and model template are rescued. In view of template up gradation, the candidate template which resembles to the model template has revealed the best match by means of Doyle’s distance. Further, the bounding box is appended over the object with the help of spatial information of updated template.


Color Optical Flow, Horn-Schunk, BFB, Regression lines, Lukas-Kanade, Sobel operator, Doyle’s distance measure, object tracking.

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