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Video-based Vehicle Detection and Tracking Technology Research

Author: LiuXueLian
Tutor: ZhangHang
School: Central South University
Course: Control Science and Engineering
Keywords: Vehicle Detection Vehicle Tracking Gaussian mixture background modeling Interframe difference Regional feature tracking
CLC: TP274.4
Type: Master's thesis
Year: 2009
Downloads: 247
Quote: 1
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Abstract


In order to solve the problems caused by the rapid development of the ground transportation , intelligent transportation systems (Intelligent Transportation System, ITS) research is referred to the important position . Vehicle detection and tracking has become a research focus of the current ITS ITS is the most basic and critical component of . Video image as the research object , vehicle detection and tracking of issues related study , the main work is reflected in the following aspects : ( 1) a fusion frame difference Gaussian mixture background model and update this method can be quickly and accurately reconstructed from the video sequence backgrounds. The algorithm uses the Gaussian mixture background model update method can be used to overcome the light changes , the environment , the impact of the interference of the background model , integration into the inter-frame difference method can effectively solve the shortcomings of poor Gaussian mixture model for real-time , with a strong robustness. ( 2) on the basis of the background modeling foreground region extraction and segmentation , and morphological filtering to eliminate noise removal and empty on the prospects . Based on the principle of shadow formation , the video frame RGB image conversion to HSV color space , and then proposed an HSV color space adaptive shadow detection algorithm . The algorithm has better real-time , able to adapt to changes in light and shadow . (3) According to the way of the moving target appears in the scene , there is given the state of the three movements of the moving target , discussed the relationship of the vehicle position in the video sequence , and given on the basis of the vehicle tracking strategy . Kalman filter in order to meet the requirements of real-time , and extended the filter the extended filter takes into account not only the current characteristics of the target , and also to increase the predictive value , and be able to more accurately track and predict target . Characteristics for vehicle tracking , and collection of geometric parameters and mean gray color parameters , using the similarity operator tracking multi- target , multi - target vehicle track , eliminating the occlusion problem to some extent . The experimental results show that the proposed algorithm can effectively vehicle target from a video sequence to identify , isolate and track with strong real-time and robustness .

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Data processing, data processing system > Centralized testing and roving detection system
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