Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/8132
Title: | Cooperative windowing for real-time visual tracking |
Authors: | Nassif, Samer Chaker |
Advisor: | Capson, David Elbestawi, M.A. |
Department: | Electrical and Computer Engineering |
Keywords: | Electrical and Computer Engineering;Electrical and Computer Engineering |
Publication Date: | Apr-1997 |
Abstract: | <p>A new, computationally efficient windowing methodology for motion tracking is described. The proposed approach is well suited to real-time focus-of-attention applications in which regions-of-interest, or windows, are used to reduce image data rates. Applications include robot guidance, where high speed image processing is required for real-time position control in operations such as fixtureless assembly for flexible manufacturing. A hierarchy of windowing functions which includes motion detection and target detection and tracking has been developed. This has resulted in a new algorithm for corner detection in image windows, as well as a proposal for measuring the information content of an image based on corner location accuracy. The techniques have been experimentally verified with the implementation of a vision system based on a high speed digital camera, a custom-built video interface board, and a network of digital signal processors. Dynamically positioned at video frame rates, windows within the camera field-of-view are made cooperative by exchanging information among their corresponding processors to allow real-time adaptation to visual motion. A cooperative windowing scheme using two networked target tracking windows is demonstrated. Motion tracking is based on the best-case output of the simultaneous application of a feature-based algorithm applied in the first window and a model-based algorithm running in the second. The experimental results demonstrate the advantages of motion tracking using this cooperative windows approach.</p> |
URI: | http://hdl.handle.net/11375/8132 |
Identifier: | opendissertations/3362 4378 1592086 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 2.88 MB | Adobe PDF | View/Open |
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