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Video image sequence moving target acquisition and tracking

Author: CaoLiWu
Tutor: FuYong
School: Huazhong University of Science and Technology
Course: Control Theory and Control Engineering
Keywords: Get moving target Moving Target Tracking Kalman filter Mean Shift Tracking Video sequence
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 21
Quote: 0
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With the rapid increase in computer performance and image processing technology, the video image sequence moving target acquisition and tracking technology has gradually become an important field of machine vision research direction , attracting more and more interest in academics . Video image sequence moving target acquisition and tracking technology is one of many industries and fields such as human-computer interaction , traffic management, intelligent monitoring the direction of development of information technology , while it's still image analysis and image understanding basic , so research moving target acquisition and tracking technology has important theoretical significance and application value. Video image sequence moving target acquisition and tracking moving objects are divided into regional and accurately obtain stable tracking moving targets two aspects . Get the moving target , the paper first analyzes the advantages and disadvantages of several commonly used detection algorithm , based on this paper presents a fusion of color and grayscale information in the background difference method to get moving target , ie grayscale and color domains domain simultaneous background difference, think gray gamut and color changes in the domain while the threshold value is greater than the respective pixels are moving targets, and then post- processed by morphological filtering to improve detection accuracy. Moving target tracking in view of the current Mean Shift algorithm on gray background close tracking of moving targets likely to fail problem , the paper presents a blend of textures, gray and motion prediction Mean Shift algorithm to track moving targets . The first to use LBP / C texture and gray feature establish target model , and Bhattacharyya coefficient as similarity measure function , and then use the Kalman filter in the current frame prediction target areas most likely to occur , and finally in the forecast area search campaign using Mean Shift algorithm the target location . Experimental results show that the paper moving target acquisition algorithm can exclude a large number of interference information , to accurately detect the moving target area . Improved moving object tracking algorithm in target and background intensity close to the case still can track moving targets , improve tracking accuracy .

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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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