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This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different...
Dynamic scenes (e.g. waving trees, ripples in water, illumination changes, camera jitters etc.) challenge many traditional background subtraction methods. In this paper, we present a novel background subtraction approach for dynamic scenes, in which the background is modeled in a multi-resolution framework. First, for each level of the pyramid, we run an independent mixture of Gaussians models (GMM)...
This paper presents a Directional Rectangular Pattern (DRP) based complex background modeling method to detect the moving objects in a video sequence. Different from Local Binary Pattern (LBP) encoding the binary result of first-order derivative between the central point and its neighborhoods, Directional Rectangular Pattern is proposed to encode the binary result of first and second order derivative...
This paper proposes a novel 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, the TPF integral histograms are used to extract the discriminative features to represent the input video. Experimental results on the public...
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