Joint Gaussian Conditional Random Fields for Background Extraction [PDF]

Abstract

Background extraction is the important technique for video processing applications. Most existing methods tend to be unsuited when solving on complex foreground movement and surrounding illumination changes video sequences. To extract background from these video sequences, we introduce a novel background extraction method based on joint Gaussian conditional random fields (JGCRF) probabilistic model of fixed-view video sequences. The model analyzes the intra-frame and inter-frame relationship to reduce the visual artifacts coming from surrounding illumination changes. Additionally, a motion-less patch extraction technique based on the sum of squared distance between corresponding pixels in two consecutive frames is proposed to solve the complex foreground movement problem. As shown in the experiments, our method achieve better background extraction results compared to the existing methods. Further, JGCRF can achieve high accuracy and high speed simultaneously when dealing with full high-definition format video sequences.