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The Object Tracking Algorithm Research Based on Mean Shift

Author: LiPengFei
Tutor: JiangGuang
School: Xi'an University of Electronic Science and Technology
Course: Communication and Information System
Keywords: Target tracking Target model Mean Shift Algorithm Harris detection
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 834
Quote: 12
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Target tracking in a video sequence for the most part with the specified target , is an important research in computer vision . A wide range of applications in video surveillance , human-machine interface , augmented reality , goal - based video compression , and many other fields . In many tracking algorithms , Mean Shift algorithm , due to its strict theory , simpler and better tracking performance in recent years, to receive widespread attention . The main object of study of this paper is based on Mean Shift tracking algorithm . Mean Shift algorithm is a feature-based modeling method of the probability density statistics . In the tracking process , the target region is usually selected in the first frame of the video sequence by the user , and to establish the corresponding target histogram . Bhattacharyaa similarity in subsequent frames , Mean Shift algorithm iteratively search for the best candidate region of the target model . This method makes Mean Shift algorithms showed good performance in the trace , such as: real - time and good , robust of occlusion, the target deformation . But when the target and the background is too similar to poor separability , the modeling approach is difficult to distinguish between the object and the background , resulting algorithm tracking performance . This selection of different video sequences on the performance of the Mean Shift tracking algorithm , theoretical analysis and experimental verification . Easily lost in a complex scene in order to improve the Mean Shift algorithm target the shortcomings of the Harris-Mean Shift algorithm based corner extraction . The algorithm corner extraction , tracking area that the main features of the target point is selected to establish the target model , the Mean Shift tracking performance of the algorithm has been greatly improved . The experimental results show that Harris-Mean Shift algorithm not only can well suppress background noise interference targeting complex scenes , and less affected by light .

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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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