Adaptive window method with sizing vectors for reliable correlation-based target tracking

摘要

We propose an adaptive window method that can provide a tracker with a tight reference window by adaptively adjusting its window size independently into all four side directions for enhancing the reliability of correlation-based image tracking in complex cluttered environments. When the size and shape of a moving object changes in an image, a correlator often accumulates walk-off error. A success of correlation-based tracking depends largely on choosing the suitable window size and position and thus transferring the proper reference image template to the next frame. We generate sizing vectors from the corners and sides, and then decompose the sizing vector from the corner into two corresponding sides. Since our tracker is capable of adjusting a reference image size more properly, stable tracking has been achieved minimizing the influence of complex background and clutters. We tested the performance of our method using 39 artificial image sequences made of 4260 images and 45 real image sequences made of more than 3400 images, and had the satisfactory results for most of them.