DocumentCode
441661
Title
Self-Adjusted Tracker Based on Genetic Neural-Networks for Tracking Multi-Target
Author
Fu, Xiao-Wei ; Fang, Kang-Ling ; Li, Xi
Volume
1
fYear
2005
fDate
18-21 Aug. 2005
Firstpage
662
Lastpage
664
Abstract
Neural-networks technique is used to establish a self-adjusted compensator for tracing moving multi-object based on the sampled images. A novel genetic algorithm (NGA) is applied to optimize the weights of neural network rapidly. The algorithm is used for tracking the moving peoples. The results of simulation and experiment are given in the end. The validity of the algorithm is demonstrated.
Keywords
Neural-networks; hybrid genetic algorithm (HGA); self-adjusted compensator; Educational institutions; Fuel cells; Genetic algorithms; Image recognition; Information science; Layout; Monitoring; Neural networks; Statistics; Transportation; Neural-networks; hybrid genetic algorithm (HGA); self-adjusted compensator;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location
Guangzhou, China
Print_ISBN
0-7803-9091-1
Type
conf
DOI
10.1109/ICMLC.2005.1527027
Filename
1527027
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