Moving object detection under complex-background is always a challenging issue

and in order to defend these challenges

this paper proposed an algorithm named MMBM (Moving object detection based on Multi-layer Background Model).First

samples are selected from neighbors of each pixel of the first frame to initialize background model.Only one frame image is needed for initialization.Second

in order to update the background model adaptively

random sampling technique is introduced

i.e.

selecting one code word randomly from the background model and then updating it with new background pixel

which overcomes the deficiency of the wrong classified code word overstaying in the background model.Multi-layer background model is proposed in order to overcome the influence of multi-disturbance in dynamic background

in which one pixel is tested through multi-layers

so as to guarantee and improve the accuracy of background pixels.Finally

Experimental results show that this algorithm can overcome the influence of multi-disturbance existing in dynamic outside scenes effectively

and at the same time

achieve a higher detection rate and recognition rate over the existing classical algorithms.