Review of moving target detection algorithms for UAV video images

Moving target detection is the basis for tasks such as target tracking, traffic monitoring and behavior analysis. However, in the video images captured by UAV, objective factors such as drone movement, propeller rotation or wind will affect the detection of moving targets, these uncertainties may cause failure during the detection. It is very important to reduce the interference, improve the detection accuracy, and make the UAV play an important role in the field of motion detection in the information age. Compared with the traditional moving target detection, the detection method of UAV video image is basically consistent with many interference factors. In this paper, the algorithm and its improvement for UAV video image moving target detection are summarized, including traditional algorithms such as motion estimation algorithm, frame difference method, background modeling method, optical flow method, and new algorithms appearing in recent years. The advantages, disadvantages and application scenarios of the above methods have been compared through the division of UAV movement status. The frame difference method is more suitable for the data of UAV hover state, the background modeling method, the optical flow method and the new algorithm can be used to deal with the UAV hover and cruise state data. None of them can solve the problem of false detection and missed inspection caused by illumination change. For processing UAV video data, it is necessary to select an appropriate algorithm according to its motion information and data characteristics to obtain good detection results.