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Video-Based Detection of Abnormal Behavior in the Examination Room

Author: LuYong
Tutor: HeDongJian
School: Northwest University of Science and Technology
Course: Applied Computer Technology
Keywords: Abnormal behavior Video Segmentation Motion detection Time and space template Template matching
CLC: TP274
Type: Master's thesis
Year: 2010
Downloads: 130
Quote: 1
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Abstract


Electronic the invigilator system in our country has been widely used , but the examination room video surveillance still use manual observation and recording methods . Human surveillance is not only time-consuming , prone to undetected presence of subjectivity and inaccuracies defects and the judgment result . In this paper, the examination room video analysis applications demand , combined with computer vision technology , video analysis techniques and pattern recognition techniques to study effectively identify abnormal behavior based on video analysis of the examination room . This paper studies the work and conclusions are as follows : (1) intelligent monitoring application needs of the examination room , and the overall program to identify abnormal behavior of video - based exam . Design of hardware and software components of the system , as well as related settings to determine a reasonable and effective examination room video capture program . Against the background of chaos , interfere with movement detection target exist segmentation difficulties caused by partial occlusion problem , improve the segmentation accuracy analysis , comparing the advantages and disadvantages of various video segmentation method based on the use of space-time integration of the video effectively split method. ( 2 ) study using interactive video segmentation method to effectively split the training video , and to extract 3D space-time template . The interactive segmentation joined human intervention to be able to make a precise segmentation of video moving target boundary . Research based on unsupervised clustering method split test video . Calculated for the large, slow implementation proposed 8 -tree pretreatment and hierarchical clustering methods , improve segmentation efficiency . The experimental results show , the use of improved Mean Shift clustering algorithm in the case does not affect the effect of segmentation , segmentation efficiency by 2-3 orders of magnitude . (3) based on the shape of the space-time template matching method . Match in the texture - rich region prone to mismatching problem , using a combination of optical flow information supplement . Experimental results show that the improved method to reduce the error rate . ( 4 ) The choice of the Linux operating system , using the Matlab simulation system and the C programming language , combined with the Intel OpenCV design video-based exam system of intelligent detection of abnormal behavior , efficient video segmentation , detection and recognition of video moving objects .

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