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Real-Time Tracking with Non-Rigid Geometric Templates Using the GPU

Ohmer, Julius F., Maire, Frederic D., & Brown, Ross A. (2006) Real-Time Tracking with Non-Rigid Geometric Templates Using the GPU. In Computer Graphics, Imaging and Visualisation 2006 International Conference on, Sydney.

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Abstract

The tracking of features in real-time video streams forms the integral part of many important applications in human-computer interaction and computer vision. Unfortunately tracking is a computationally intensive task, since the video information used by the tracker is usually prepared by applying a series of image processing filters. Thus it is difficult to realize a real-time tracker using only the CPU of a standard PC. Over the last few years, commodity Graphics Processing Units (GPU) have evolved from fixed graphics pipeline processors into more flexible and powerful data-parallel processors. These stream processors are capable of sustaining computation rates of greater than ten times that of a single CPU. GPUs are inexpensive and are becoming ubiquitous (desktops, laptops, PDAs, cell phones). They are now capable to greatly relieve the CPU especially for large-scale parallel processing tasks, which map well to the architecture of the GPU. In this paper, we present a system, which uses a gradient vector field to track features with flexible geometric templates. Our implementation is specifically designed to suit the parallel processing architecture of the GPU. It is capable to achieve realtime performance with framerates of around 30 frames per second.

Impact and interest:

2 citations in Scopus
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340 since deposited on 14 Sep 2006
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ID Code: 5013
Item Type: Conference Paper
Keywords: Computer Vision, Computer Graphics, GPU Programming, Sanke Models
DOI: 10.1109/CGIV.2006.75
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Graphics (080103)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2006 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 14 Sep 2006
Last Modified: 29 Feb 2012 23:22

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