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.
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:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Computer Vision, Computer Graphics, GPU Programming, Sanke Models|
|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 13:22|
Repository Staff Only: item control page