Tracking Objects From Satellite Videos: A Velocity Feature Based Correlation Filter

Satellite video target tracking is a new topic in the remote sensing field, which refers to tracking moving objects of interest from satellite video in real time. The target of interest usually occupies only a few pixels in a satellite video image, even when the train is long. Thus, satellite video target tracking still faces new challenges compared with traditional visual tracking, including the detection of low-resolution targets, features with less representation, and targets with an extremely similar background. Little research has been done on satellite video target tracking, and little is known about whether or not the existing tracking algorithms can still work on the satellite video data. This paper, for the first time, intensively investigated 13 typical trackers in traditional visual tracking. The experimental results suggest that most of the state-of-the-art tracking algorithms mainly rely on luminance, color features, or convolutional features, and they fail to track satellite video targets due to their inadequate representation features. To overcome this difficulty, we propose a velocity correlation filter (VCF) algorithm, which employs both a velocity feature and an inertia mechanism (IM) to construct a specific kernel correlation filter for the satellite video target tracking. The velocity feature has a high discriminative ability to detect moving targets in satellite videos, and the IM can prevent model drift adaptively. Experimental results on three real satellite video data sets show that the VCF outperforms state-of-the-art tracking methods with regard to precision and success plots while running at over 100 frames per second.