Improved visual background extractor using an adaptive distance threshold

Abstract

Camouflage is a challenging issue in moving object detection. Even the recent and advanced background subtraction technique, visual background extractor (ViBe), cannot effectively deal with it. To better handle camouflage according to the perception characteristics of the human visual system (HVS) in terms of minimum change of intensity under a certain background illumination, we propose an improved ViBe method using an adaptive distance threshold, named IViBe for short. Different from the original ViBe using a fixed distance threshold for background matching, our approach adaptively sets a distance threshold for each background sample based on its intensity. Through analyzing the performance of the HVS in discriminating intensity changes, we determine a reasonable ratio between the intensity of a background sample and its corresponding distance threshold. We also analyze the impacts of our adaptive threshold together with an update mechanism on detection results. Experimental results demonstrate that our method outperforms ViBe even when the foreground and background share similar intensities. Furthermore, in a scenario where foreground objects are motionless for several frames, our IViBe not only reduces the initial false negatives, but also suppresses the diffusion of misclassification caused by those false negatives serving as erroneous background seeds, and hence shows an improved performance compared to ViBe.