Video Sequences Foreground Enhancement Using Hidden Markov Model

Abstract

Foreground detection is an important part in video surveillance system. The detection results will significantly affect the performance of tracking, abnormal behavior analysis and other following procedures. Many algorithms have been proposed to improve the detection performance. However, these algorithms simply focus on one single frame, ignoring the relationship among the detection results of one target in successive frames. This paper presents a novel foreground enhancement algorithm using Hidden Markov Model (HMM). In a video sequence, one target in successive frames usually has similar shape, size, et al. With this property, the target can be modeled by HMM and enhanced using the result of its prior frame. The observation of HMM is obtained by ViBe. The enhancement result is then estimated by using Maximum A Posteriori (MAP). Experimental results show that compared with the state-of-art algorithm, the proposed method can enhance foreground detection effectively.