Object Tracking in Satellite Videos: Correlation Particle Filter Tracking Method With Motion Estimation by Kalman Filter

Abstract

Object tracking in satellite videos faces various challenges such as target occlusion, target rotation, and background clutter. This study proposes a correlation particle filter algorithm with motion estimation for object tracking in satellite videos. The tracker, called CPKF, combines the strengths of the correlation, particle, and Kalman filters. Compared with existing tracking methods based on correlation filters, the proposed tracker has three major advantages: (1) Particle sampling, and motion estimation build robustness against partial and complete occlusion. (2) Color histogram model makes it robust to target rotation. (3) Fusion of multiple feature response maps effectively handle background clutter and low contrast. The experimental results demonstrate that the proposed tracking algorithm performs better than state-of-the-art methods.