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

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

Automated summary

This study proposes a satellite video object tracking algorithm that improves robustness through particle sampling and motion estimation, enhances robustness to target rotation through the use of a color histogram model, and effectively handles background clutter and low contrast by fusing multiple feature response maps.