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Crowd density estimation is important in crowd analysis and texture analysis is an efficient method to estimate crowd density, this paper proposes an improved estimation approach based on texture analysis. First, background is removed by using a combination of optical flow and background subtract method. Then according to texture analysis, a set of new feature is extracted from foreground image. Finally,...
The foreground detection is a key processing in crowd motion analysis containing abnormal behavior detections and crowd density estimations. This paper proposes a new foreground detection approach called optical flow and background model (OFBM) based on Lucas-Kanade optical flow and Gaussian background model methods. This approach overcomes the shortages of optical flow and background subtract. Experimental...
Background subtraction is a crucial step in many automatic video content analysis applications. While numerous acceptable techniques have been proposed so far for background extraction, there is still a need to produce more efficient algorithms in terms of adaptability to multiple environments, noise resilience, and computation efficiency. In this paper, we present a powerful method for background...
We propose a novel technique for estimating the number of people in a video sequence; it has the advantages of being stable even in crowded situations and needing no ground-truth data. By analyzing the geometrical relationships between image pixels and their intersection volumes in the real world quantitatively, a foreground image can be directly indicate the number of people. Because foreground detection...
This paper presents a new approach to estimate the optical flow problem. Many estimation methods have been proposed, but instabilities at boundaries of moving objects still presents a challenge in assessing optical flow. In this approach, we propose a new algorithm which is used to detect and readjust quickly unstable regions in result of estimating optical flow. Besides, we propose an anisotropic...
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