Selective-Attention Correlation Measure for Precision Video Tracking

摘要

In this paper, the false-peaks problem of the conventional correlation-based video tracking is investigated using a simple mathematical analysis. To reduce the false detection problem, a selective-attention correlation measure is proposed. The problem with the conventional correlation measures is that all pixels in the reference block image are equally treated in the computation of the correlation measures irrespective of target or background pixels. Therefore, the more the reference block image includes background pixels, the higher probability of false-peaks is introduced due to the correlation between the background pixels of the reference block and those of the input search image. The proposed selective-attention correlation measure has different consideration according to target and background pixels in the matching process, which conform with the selective-attention property of human visual system. Various computer simulations validated these analyses and confirmed that the proposed selective-attention measure is effective to reduce considerably the probability of the false-peaks.