This paper focuses on applying an interval recursive least-squares (RLS) filter to a video target tracking problem. This is to circumvent the potential limitation of a RLS filter due to its sensitivity to variations in filter parameters and disturbances to state observations. Such sensitivity can make the solutions invalid in practical problems. In particular, in the application of video target tracking using a RLS filter, inaccurate parameters in the affine model may result in noticeable deviations from true target positions sufficient to lose a target. An interval RLS filter is proposed to produce state estimation and prediction in 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 it 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 errors in the affine models, and outperforms that using a RLS filter. Performance evaluations using real-world video sequences are provided to demonstrate the effectiveness of the proposed algorithm.
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.
This paper addresses the issue of tracking partially occluded targets in videos recorded by moving cameras of
either handhold or airborne. We propose a fast geometric constraint global motion algorithm to reduce the
computation overhead dramatically and the effect caused by outliers from moving targets. A recursive least-squares
filter with forgetting factor is utilized to filter out disturbances and to provide a better estimation of
the target's position in the current frame as well as the prediction of the position and velocity for the next
frame. The filter uses the affine model and the primary search result to construct a kinetic model. After that,
a compact search region is formed based on the prediction to reduce mismatch and improve computation speed.
The adaptive template matching is applied to improve the performance further. With these important steps, a
tracking algorithm is developed and tested on real video sequences.
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