@article{(Open Science Index):https://publications.waset.org/pdf/1611,
	  title     = {Using Mean-Shift Tracking Algorithms for Real-Time Tracking of Moving Images on an Autonomous Vehicle Testbed Platform},
	  author    = {Benjamin Gorry and  Zezhi Chen and  Kevin Hammond and  Andy Wallace and  Greg Michaelson},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper describes new computer vision algorithms
that have been developed to track moving objects as part of a
long-term study into the design of (semi-)autonomous vehicles. We
present the results of a study to exploit variable kernels for tracking in
video sequences. The basis of our work is the mean shift
object-tracking algorithm; for a moving target, it is usual to define a
rectangular target window in an initial frame, and then process the data
within that window to separate the tracked object from the background
by the mean shift segmentation algorithm. Rather than use the
standard, Epanechnikov kernel, we have used a kernel weighted by the
Chamfer distance transform to improve the accuracy of target
representation and localization, minimising the distance between the
two distributions in RGB color space using the Bhattacharyya
coefficient. Experimental results show the improved tracking
capability and versatility of the algorithm in comparison with results
using the standard kernel. These algorithms are incorporated as part of
a robot test-bed architecture which has been used to demonstrate their
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {10},
	  year      = {2007},
	  pages     = {2990 - 2995},
	  ee        = {https://publications.waset.org/pdf/1611},
	  url   	= {https://publications.waset.org/vol/10},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 10, 2007},