A two-stage foreground propagation for moving object detection in a non-stationary

Abstract

In this paper, we propose a two-stage foreground propagation that uses clues to adapt to the environment and detect moving objects in a non-stationary camera. The first stage creates a weight matrix to instantaneously regulate the background model by responding to clues from frame differencing and background subtraction. The regulated background model is less affected by inaccurate motion compensation. In the second stage, an iterative approach is taken to refine the threshold for each pixel location by initially using pixels with high foreground probability as clues. Foreground regions detected from the refined threshold are less likely to be false detections and capture true object regions with completeness. Experimental results showed that the two-stage foreground propagation had significantly higher recall with comparable precision and outperformed other methods.