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Proceedings Paper

Interval least-squares filtering with applications to video target tracking
Author(s): Baohua Li; Changchun Li; Jennie Si; Glen Abousleman
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Paper Abstract

This paper focuses on applying an interval recursive least-squares (RLS) filter to a video target tracking problem. An RLS filter can be sensitive to variations in filter parameters and disturbance to state observations to make the solutions impractical in practical problems. Specially, in the application of video target tracking using an RLS filter, inaccurate parameters in the affine model may result in noticeable deviations from true target positions to lose the target. To make results robust, each filter parameter and state observation is allowed to vary in an interval. Motivated by this idea, an interval RLS filter is proposed to produce state estimation and prediction by narrow intervals. Simulations show that an interval RLS filter is robust to state and observation noise and variations in filter parameters and state observations, and outperforms an interval Kalman filter. Using an interval RLS filter, a video target tracking algorithm is developed to estimate the target position in each frame. The proposed tracking algorithm using an interval RLS filter is robust to noise in video sequences and error of the affine models, and outperforms that using an RLS filter. Performance evaluations using real-world video sequences are provided to demonstrate effectiveness of the proposed algorithm.

Paper Details

Date Published: 17 April 2008
PDF: 12 pages
Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 69681D (17 April 2008); doi: 10.1117/12.777226
Show Author Affiliations
Baohua Li, Arizona State Univ. (United States)
Changchun Li, Arizona State Univ. (United States)
Jennie Si, Arizona State Univ. (United States)
Glen Abousleman, General Dynamics C4 Systems (United States)


Published in SPIE Proceedings Vol. 6968:
Signal Processing, Sensor Fusion, and Target Recognition XVII
Ivan Kadar, Editor(s)

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