Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker
This addresses the challenge of object tracking in satellite videos for remote sensing applications, but it is incremental as it builds on existing optical flow methods.
The authors tackled the problem of tracking small, background-similar objects in satellite videos by proposing a multi-frame optical flow tracker (MOFT), which achieved more accurate tracking compared to state-of-the-art algorithms on three VHR remote sensing datasets.
Object tracking is a hot topic in computer vision. Thanks to the booming of the very high resolution (VHR) remote sensing techniques, it is now possible to track targets of interests in satellite videos. However, since the targets in the satellite videos are usually too small compared with the entire image, and too similar with the background, most state-of-the-art algorithms failed to track the target in satellite videos with a satisfactory accuracy. Due to the fact that optical flow shows the great potential to detect even the slight movement of the targets, we proposed a multi-frame optical flow tracker (MOFT) for object tracking in satellite videos. The Lucas-Kanade optical flow method was fused with the HSV color system and integral image to track the targets in the satellite videos, while multi-frame difference method was utilized in the optical flow tracker for a better interpretation. The experiments with three VHR remote sensing satellite video datasets indicate that compared with state-of-the-art object tracking algorithms, the proposed method can track the target more accurately.