A noise robust method for change detection
Motion and other changes in video sequences can be detected by analyzing the differences between grey levels of successive frames. The simplest method to do this, is to subtract the grey values of corresponding pixels of two consecutive frames. This is a pixel based technique: if there is a difference, the pixel in question is considered to be on or near to a moving object. This assumption is clearly not always correct because changes in grey scale may also be caused by noise. In this paper, we present a technique for change detection that has low noise-sensitivity and that has a low complexity. The technique combines recursive temporal filtering with local neighborhood conditioning.