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Background subtraction is commonly used to detect foreground objects in video surveillance. Traditional background subtraction methods are usually based on the assumption that the background is stationary. However, they are not applicable to dynamic background, whose background images change over time. In this paper, we propose an adaptive Local-Patch Gaussian Mixture Model (LPGMM) as the dynamic...
This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly...
The main objective of this paper is to develop multiple human object tracking approach based on motion estimation and detection, background subtraction, shadow removal and occlusion detection. A reference frame is initially used and considered as background information. While a new object enters into the frame, the foreground information and background information are identified using the reference...
An adaptive shadow removal algorithm with background subtraction method based on shadow position and edges attributes is presented. Accumulative frames differences of symmetrical frames are adopted to detect the moving regions approximately, and the moving regions templates are constructed through static index, then the background model is built by the statistics of brightness information. On the...
This paper presents a novel texture-edge descriptor, TED, for background modeling and pedestrian detection in video sequences which models texture and edge information of each image block simultaneously. Each image block is modeled as a group of adaptive TED histograms that are calculated for pixels of block over a rectangular neighborhood. TED is an 8-bit binary number which is independent of the...
Detection of moving objects is the first step in many applications using video sequences like video-surveillance, optical motion capture and multimedia application. The process mainly used is the background subtraction which one key step is the foreground detection. The goal is to classify pixels of the current image as foreground or background. Some critical situations as shadows, illumination variations...
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