Needles in a Haystack: Tracking City-Scale Moving Vehicles From Continuously Moving Satellite

In recent years, the satellite videos have been captured by moving satellite platforms. In contrast to consumers, movies, and common surveillance videos, satellite videos can record the snapshots of city-scale scenes. In a broad field-of-view of satellite videos, each moving target would be very tiny and usually composed of several pixels in frames. Even worse, the noise signals also exist in the video frames, and the background of the video frames subpixel-level and uneven moving thanks to the motion of satellites. We argue that it is a novel type of computer vision task since previous technologies are unable to detect such tiny moving vehicles efficiently. This paper proposes a novel framework that can identify small moving vehicles in satellite videos. In particular, we offer a novel detecting algorithm based on the local noise modeling. We differentiate the potential vehicle targets from noise patterns by an exponential probability distribution. Subsequently, a multi-morphological-cue based discrimination strategy is designed to distinguish correct vehicle targets from the existing noises further. Another significant contribution is to introduce a series of evaluation protocols to measure the performance of tiny moving vehicle detection systematically. We annotate satellite videos manually to test our algorithms under different evaluation criterions. The proposed algorithm is also compared with the state-of-the-art baselines, which demonstrates the advantages of our framework over the benchmarks. Besides, the dataset would be downloaded from http://first.authour.github.com.