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Video surveillance study in moving target detection and tracking technology

Author: SunLei
Tutor: KuangPing
School: University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: moving target detection background difference method moving target tracking SIFT feature matching
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 216
Quote: 4
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Abstract


Video surveillance technology is utilized more and more frequently in modern society. The new generation intelligent video surveillance is proposed to resolve the problem that the traditional video surveillance is heavy, tedious, and all-day artifical care. Therefore, the moving targets detection and tracking, which are amplified as key-point technology, have great significance for intelligent video surveillance system. The main research in this article is listed as follows:First, the common image processing techniques are introduced briefly, and then an improved multi-stage median filter technology was presented to eliminate the noise affecting the moving target detection result, which combines advantages of median filtering and multi-level median filtering. It can be applied for video image noise processing, and the effectiveness of which can be verified in experiments.For the moving target detection, firstly, three common algorithms in motion detection are introduced and compared, including the optical flow calculation method, frame difference method, background difference method, and two main background modeling methods: single Gaussian Model method and Gaussian Mixture Model method are introduced. Some research is accomplished on the adaptive threshold in image binary and the target shadow removing. On the basis of these, Gaussian Mixture Model method and the background difference method are combined with to detect the moving target in video images. It is shown in experiments that the algorithm can extract the moving target area completely and quickly.For the moving target tracking, several common target tracking algorithms are introduced and summarized, including their advantages and disadvantages. The key point is focus on the SIFT feature matching algorithm, especially the generation and matching procedure of feature descriptor. Although it owns excellent stability and uniqueness, it can not be applied in real-time environment for complex calculation and time consumer. Therefore, an improved low-latitude SIFT moving target tracking algorithm is proposed in this paper, which only extract moving target region of the current video image, rather than the whole one, moreover the dimension of descriptor vector is reduced from 128 to 32, and the time consuming is reduced by half while matching accuracy is constant. It can fulfill the target successfully in real-time when video frame rate is 10-15, and its effectiveness was proved in the experiment while distinct moving trace can be obtained.Finally, a simple intelligent video surveillance platform is designed and accomplished in the Visual C++ 6.0 development platform, meanwhile the OpenCV library is integrated. It can achieve the moving target detection and tracking, including some simple applications.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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