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Sequential Monte Carlo Particle Filter and It’s Applications in Visual Tracking

Author: LuoRui
Tutor: LiuGuiXi
School: Xi'an University of Electronic Science and Technology
Course: Control Theory and Control Engineering
Keywords: Video tracking Particle Filter Feature subspace Incremental learning
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 288
Quote: 3
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Sequential Monte Carlo particle filter technology is a real-time reasoning algorithm , because of its nonlinear problems to deal effectively with broad application prospects , has been more and more attention , become statistically , signal processing , automatic control , artificial intelligence , computer vision , and other fields , new research hotspot . Although there has made ??some research in the theory and application , but the development of the particle filter is still in its infancy , and many of the key technology still not recognized as a valid solution . In this paper, the key to improving the performance of the particle filter technology and its application in video tracking . In improving its performance degradation problems, mainly present in the particle filter weights importance density function selection problem have been studied . In this paper, in terms of video tracking Adaptive Particle Filter video tracking algorithm based on incremental learning . This algorithm has three kinds of characteristics . First , online learning feature subspace basis vectors ; the second RSR resampling method adaptively adjusted according to the particles weight and the size of the number of particles ; Third, according to the particle weight and size of the target feature subspace update methods of the base vector of the adaptive adjustment . The experiments show that in the target appearance , posture and lighting conditions have a greater change in circumstances , the proposed algorithm can maintain a higher tracking accuracy , robustness .

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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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