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Research at St Andrews

Using Mean-Shift Tracking Algorithms for Real-Time Tracking of Moving Images on an Autonomous Vehicle Testbed Platform

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Benjamin Gorry, Zezhi Chen, Kevin Hammond, Andy Wallace, Greg Michaelson

School/Research organisations


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 effectiveness.



Original languageEnglish
Title of host publicationWASET - World Academy of Science, Engineering and Technology, November 2007
EditorsC. Ardil
PublisherWorld Academy of Science, Engineering and Technology,
Number of pages6
Publication statusPublished - 2007
EventConference of the World Academy of Science Engineering and Technology - Venice, Italy
Duration: 23 Nov 200725 Nov 2007

Publication series

NameWASET Proceedings
ISSN (Print)1307-6884


ConferenceConference of the World Academy of Science Engineering and Technology

    Research areas

  • Hume, functional programming, autonomous vehicle, pioneer robot, vision

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