Aiming at the problems of weak anti-interference ability and poor speed of dynamic foreign object detection in complex railway environment,a rail foreign object intrusion detection method based on an adaptive Gaussian mixture model (AGMM) is put forward in this paper.By analyzing the characteristics of randomness when compound jitter occurs in railway scenes,Firstly,jitter detection on the input railway video is performed,and then affine transformation and median filtering are introduced to process the jittery images in the video sequence.Secondly,the method of iterative filling frame by frame is used to fill the black edges of the image after debounce to obtain a railway video frame without jitter and without black edges.Finally,on the basis of the existing Gaussian mixture model,an adaptive selection of the number of Gaussian distributions and learning rate is designed,and the improved Gaussian mixture model is used to realize the background modeling of complex railway videos,and thereby improve the detection speed of foreground objects.The experimental results show that in the case of jitter in the railway video,the accuracy rate of the track foreign body intrusion target detection is 2.6 times,and the detection speed is 2.8 times that of the original algorithm,which can improve the anti-interference and rapidity of target detection.