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In recent years, the satellite videos have been captured by a movingsatellite platform. In contrast to consumer, movie, and common surveillancevideos, satellite video can record the snapshot of the city-scale scene. In abroad field-of-view of satellite videos, each moving target would be very tinyand usually composed of several pixels in frames. Even worse, the noise signalsalso existed in the video frames, since the background of the video frame hasthe subpixel-level and uneven moving thanks to the motion of satellites. Weargue that this is a new type of computer vision task since previoustechnologies are unable to detect such tiny vehicles efficiently. This paperproposes a novel framework that can identify the small moving vehicles insatellite videos. In particular, we offer a novel detecting algorithm based onthe local noise modeling. We differentiate the potential vehicle targets fromnoise patterns by an exponential probability distribution. Subsequently, amulti-morphological-cue based discrimination strategy is designed todistinguish correct vehicle targets from a few existing noises further. Anothersignificant contribution is to introduce a series of evaluation protocols tomeasure the performance of tiny moving vehicle detection systematically. Weannotate a satellite video manually and use it to test our algorithms underdifferent evaluation criterion. The proposed algorithm is also compared withthe state-of-the-art baselines, and demonstrates the advantages of ourframework over the benchmarks.

Detecting Tiny Moving Vehicles in Satellite Videos