The invention discloses a moving object detection method based on a self-adapting kernel density estimation model. The method includes the steps that a background model based on kernel density estimation is built according to input videos, and accordingly the width of a probability density function of pixels of the input videos and the width of a kernel function are obtained; according to the width of a probability density function of gray values of the pixels of the input videos and the width of the kernel function, a probability value interval of a sample point is determined, then probabilities of the gray values of all the pixels are traversed, and accordingly a probability distribution histogram is formed; secondary linear interpolation and small threshold difference are carried out on the probability distribution histogram, and accordingly a difference histogram is obtained; a foreground threshold and a background threshold are solved in a self-adaptive mode according to the difference histogram; the built background model is updated according to the probability values of the gray values of the pixels of the input videos, the foreground threshold and the background threshold. The method has the advantages of being little in calculation quantity, good in instantaneity and few in error, and can be widely used in the field of computer visual analysis.