Tiny moving vehicle detection in satellite video with constraints of multiple prior information

ABSTRACT

Ｗith the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information this paper, a tiny vehicle detection method based on spatio-temporal information is proposed to constrain the significance of the image. Firstly, the background modelling method is used to obtain the motion heat map of the image and constrain the motion region. A significance detection method for small targets was used to obtain the significance mapping of these regions. Finally, the detection results were optimized by combining the significance neighbourhood information and the time information between frames to output the binary target detection map. Finally, taking different urban road scenes in ‘Jilin-1’satellite video as examples and compares a variety of existing algorithms. Experiments prove that the proposed algorithm can maintain false alarm rate of less than 10% when the detection accuracy and recall rate reach 85% and has certain anti-interference ability in the image environment with satellite Angle deviation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant No. 41771457 and the Research Program of the Department of Natural Resources of Hubei Province of China [No. ZRZY2020KJ03].