The invention provides an improved adaptive Gaussian mixture foreground detection method. The method comprises: firstly, performing learning by utilizing a Gaussian mixture model to form an initialized Gaussian mixture background model; secondly, for a new input video sequence, performing sampling at an interval of N frames, obtaining an image frame by utilizing weighted time-domain mean filtering, and performing background model updating by taking the image frame as an input of Gaussian mixture modeling; automatically determining whether background mutation exists in a current frame by Poisson distribution, if the background mutation does not exist, keeping normal sampling interval and learning rate, otherwise, reducing an interval frame number and increasing the learning rate, updating the background model, and extracting a current background frame; and finally, performing difference by utilizing the current frame and the current background frame, obtaining an adaptive threshold with a maximum entropy method, performing weighted mean on the obtained threshold, and performing foreground detection. According to the method, motion interferences of tree leaf shake, water ripples and the like in a video scene are effectively overcome, the calculation amount of frames is reduced through periodic sampling, and the timeliness is improved.