Complex background modeling based on Texture Pattern Flow with adaptive threshold propagation

This paper proposes a high-order Texture Pattern Flow (TPF) for complex background modeling and motion detection. The pattern flow is proposed to encode the binary pattern changes among the neighborhoods in the space–time domain. To model the distribution of the TPF pattern flow, the TPF integral histograms are used to extract the discriminative features to represent the input video. The Gaussian Mixture Model (GMM) is exploited to calculate an adaptive threshold in propagation way for the histogram similarity measure to decide which part/pixel is background or moving object. Experimental results on the public databases testify the effectiveness of the proposed method in comparison to LBP and GMM based background modeling methods.

► This paper proposes a high-order Texture Pattern Flow (TPF) for background modeling. ► We use the TPF integral histograms to extract features to represent the input video. ► The Gaussian Mixture Model (GMM) is exploited to calculate an adaptive threshold. ► The threshold is used to decide which part/pixel is background or moving object.