A foreground detection based video stabilization method and its application in aerospace measurement and control

Abstract

The output video of the optical equipment in the aerospace measurement and control field is prone to the problem of image quality degradation caused by the operator’s unstable manual operation. to improve the classical motion estimation based video stabilization algorithm, a novel video stabilization method based on foreground detection is proposed in this paper. Firstly, a object detection datasets based on historical images of the launch center is collected and labeled. Secondly, inspired by transfer learning and prior knowledge of the image in launch center, a YOLO-based object detection method for rocket launching scene is designed. Then, the object detection method is introduced into the motion estimation based video stabilization pipeline in which the object detection is used for foreground detection so the tracked feature points are filtered to reduce the global motion estimation error caused by the motion of the background area. Thus, the error stabilization problem in the classic motion estimation-based video stabilization method is avoided. Experiments show that the video stabilization method proposed in this paper achieved better image stabilization effect in subject and object evaluation. This paper has certain reference significance for exploring the application of deep learning and artificial intelligence technology in the field of aerospace measurement and control field.

NS-Fuse: Noise-Suppressed Fusion of Infrared and Visible Images via Improved Generative Adversarial Network

“…According to the conclusions of Sun et al [4], the current proposed fusion methods can be divided into seven categories, including methods based on multiscale transform [5][6], methods based on sparse representation [7], methods based on neural network [8], methods based on subspace [9], methods based on saliency [10], methods based on hybrid model [11] and other methods [12][13]. All of these methods have achieved good fusion results, but there are still limitations, requiring manual design of rules for combining features, and hidden defects in these rules may make it difficult to obtain reliable and well-generalized fusion models.…”