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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...
Background subtraction is an effective way which is commonly used in intelligent monitoring system for extraction of moving objects. However, the key step of background subtraction need for a precise and time-varying background model. In this paper, we describe an improved background model with its updating method, and apply to a computer vision-based motion detection system able to detect moving...
Both improper initialization and fake Gaussian components are critical problems in GMM-based foreground detection. The former can lead to a poor local maximum, while the latter invokes unhandled disturbance. To eliminate these destructive impacts, two kinds of feedback knowledge are introduced: positive and negative prior. For appropriate initialization, high level modules provide the positive prior...
Applying image processing technologies to pedestrian detection has been a hot research topic in intelligent transportation systems (ITS). However, the existing video-based algorithms to extract background image may suffer their inefficiency in detecting slow or static pedestrians. To fill the gap, an improved Gaussian mixture model (GMM) for pedestrian detection is proposed in this paper. Three novel...
Background subtraction is a widely used method for moving object detection in computer vision. It is usually applied in video surveillance systems. There are two major kinds of background subtraction approaches: pixel-based and block based. Yet there are three problems that can not be simultaneously solved by either method: the robustness to illumination changes, the effectiveness in suppressing shadows,...
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