Robust Dynamic Background Model with Adaptive Region Based on T2FS and GMM
Yunbo Guo, Yi Ji, Jutao Zhang, Shengrong Gong, Chunping Liu · Lecture notes in computer science · 2015
For many tracking and surveillance applications, Gaussian mixture model (GMM) provides an effective mean to segment the foreground from background. Though, because of insufficient and noisy data in complex dynamic scenes, the estimated parameters of the GMM, which are based on the assumption that the pixel process meets multi-modal Gaussian distribution, may not accurately reflect the underlying distribution of the observations. And the existing block-based GMM (BGMM) method may be able to segment only rough foreground objects with time-consuming calculations. To solve these difficulties, this paper proposes to use type-2 fuzzy sets (T2FSs) to handle GMM’s uncertain parameters (T2GMM). Furthermore, this paper also introduces a novel representation of contextual spatial information including the color, edge and texture features for each block which is faster and almost lossless (T2BGMM). Experimental results demonstrate the efficiency of the proposed methods.