Airborne Camera Dynamic Target Detection Based on Background Prediction and Semantic Compensation in Surface Environment

With the continuous development of Unmanned Aerial Vehicle (UAV) visual positioning technology, dynamic target detection and feature point optimization have become one of the difficult problems for UAVs to achieve high-precision visual positioning in a dynamic environment. To solve the problem of UAV target detection accuracy in a dynamic environment, this paper proposes a dynamic target detection method for airborne cameras based on background prediction and semantic compensation. Firstly, to solve the problem of high false detection rate of the traditional background difference method on the camera of moving carrier, this paper proposes a background compensation method based on region of interest prediction and uses a technique combining a scale-transformed Unscented Kalman filter (ST-UKF) and Rodrigues Formula with Perspective Transformation (RFPT) to predict the background model. Then, a moving target discrimination method based on semantic confidence is proposed to solve the problem that the traditional semantic map cannot effectively discriminate the current state of the object and leads to an excessive elimination of effective feature points; in addition, a general detection framework for airborne cameras to obtain accurate and reliable target selection boxes are proposed to improve the positioning accuracy of traditional visual positioning methods in dynamic environments, the feasibility, and innovation of the algorithm in this paper are verified through data set simulation and experimental environment.