NDLI: Research on Objective Tracking of Mean Shift Algorithm Based on Particle Swarm Optimization

Content Provider	IEEE Xplore Digital Library
Author	Hongxia Chu Kejun Wang
Copyright Year	2009
Abstract	In light of Mean Shift’s inability to update model during objective tracking process, an updating solution for models of Means Shift algorithm is proposed by utilization of Particle Swarm Optimization. This solution improves each Eigen value probability, as a single particle, in model image characteristic space by using Particle Swarm Optimization algorithm, time variations according to probability can be calculated to acquire variation of all Eigen value in models, which in turn, results in updating of models. In the solution, the combinational advantage of Particle Swarm’s global and regional search is fully utilized to acquire self-adaptable and optimal models. Experiment results indicate the solution can effectively solve models’ un-matching problems resulted from spinning and masking of moving objective so as to realize accurate and fast objective tracking and improve self-adapting ability of tracking algorithm.
Starting Page	83
Ending Page	86
File Size	543200
Page Count	4
File Format	PDF
ISBN	9780769538594
DOI	10.1109/IITA.2009.270
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	model updating Target tracking Automation Probability Educational institutions Particle tracking Particle swarm optimization Information technology Histograms Mean Shift objective tracking Spinning Kernel Particle Swarm Optimization
Content Type	Text
Resource Type	Article

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