Self-adaptive Video Target Tracking and Application based on Particle Filter

DOI

10.2991/mmebc-16.2016.437How to use a DOI?

Keywords

Simulation; Accuracy; Differential Evolution; Filtering Congestion-Degree; Tracking

Abstract

In order to promote tracking and positioning accuracy of wireless sensor network node and reduce energy consumption further, a multi-objective congestion-degree differential optimization and Bayes quantification variation filtering and estimating WSN tracking and positioning algorithm is presented in the Thesis. First of all, for positioning problem, Bayes quantification variation filtering method is adopted to estimate the next position area of the objective and quantification variation filtering method is used to select the proper positioning parameter and node and multi-objective parameter optimization model of quantification variation filtering is also designed. Secondly, as optimization accuracy of traditional multi-objective optimization algorithm is not high, multi-objective differential evolution algorithm based on individual congestion of certain population is designed so as to optimize parameters of quantification variation filtering algorithm and realize multi-objective optimization of filtering parameter. At last, experiment simulation shows that, the algorithm can realize tracking and positioning of targeted node effectively and can also save energy consumption.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Fankun Meng
AU  - Hao Tian
PY  - 2016/06
DA  - 2016/06
TI  - Self-adaptive Video Target Tracking and Application based on Particle Filter
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 2182
EP  - 2185
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.437
DO  - 10.2991/mmebc-16.2016.437
ID  - Meng2016/06
ER  -