Abstract: Abstract： To solve the problem that moving object detection is susceptible to jitter in hovering drones， leading to the generation of a significant amount of background noise and lower accuracy， a multiscale EA-KDE （MEA-KDE） background difference algorithm is proposed. This algorithm initially achieves a multiscale decomposition of image sequences to obtain a multiscale image sequence. Subsequently， before performing detection， the segmentation threshold for detection is calculated and updated by considering the area threshold and the current image frame， thereby incorporating information from the current frame. Background difference operations using high and low dual segmentation thresholds are performed on images at different scales to enhance detection robustness. Finally， a top-down fusion strategy is employed to merge the detection results from various scales， preserving the clear contours of the targets while eliminating noise. Furthermore， a proposed boundary expansion fusion post-processing algorithm helps alleviate the fragmented targets caused by detection breaks. Experimental results demonstrate that the proposed algorithm effectively suppresses background noise caused by jitter. On two real drone datasets， average F1 scores of 0.951 and 0.952 were obtained， representing improvements of 0.144 and 0.276， respectively， compared to the original algorithm.

Key words: Key words： machine vision, motion target detection, drone video, background difference algorithm, Gaussian pyramid