Interval least-squares filtering with applications to robust video target tracking

An interval recursive least-squares (RLS) filter is developed to produce state estimation and prediction by narrow intervals, in which true values are contained with high confidence. The interval filter is robust to variations of the filter parameters and state observations. Using this filter, a video target tracking algorithm is proposed to estimate the target position in each frame. The tracking algorithm is robust to both noise in the video sequence and estimation error of the affine model. The experiments show that the tracking algorithm using the interval RLS filter outperforms that using an RLS filter.