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Proceedings Paper

Fully invariant multiple object recognition and tracking using MACH and Kalman filters
Author(s): Peter Bone; Rupert Young; Chris Chatwin
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Paper Abstract

A method of recognising and tracking multiple solid objects in video sequences despite any kind of perspective distortion is demonstrated. Moving objects are initially segmented from the scene using a background subtraction method to minimize the search area of the filter. A variation on the Maximum Average Correlation Height (MACH) filter is used to create invariance to orientation while giving high tolerance to background clutter and noise. A log r-θ mapping is employed to give invariance to in-plane rotation and scale by transforming rotation and scale variations of the target object into vertical and horizontal shifts. The MACH filter is trained on the log r-θ map of the target for a range of orientations and applied sequentially over the regions of movement in successive video frames to test for target objects. A Kalman filter is employed to continuously track the target objects over successive frames, which has enabled the system to track multiple targets despite temporary occlusion or intersection.

Paper Details

Date Published: 9 April 2007
PDF: 11 pages
Proc. SPIE 6574, Optical Pattern Recognition XVIII, 65740A (9 April 2007); doi: 10.1117/12.711141
Show Author Affiliations
Peter Bone, Univ. of Sussex (United Kingdom)
Rupert Young, Univ. of Sussex (United Kingdom)
Chris Chatwin, Univ. of Sussex (United Kingdom)


Published in SPIE Proceedings Vol. 6574:
Optical Pattern Recognition XVIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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