Title :
Self-Adjusted Tracker Based on Genetic Neural-Networks for Tracking Multi-Target
Author :
Fu, Xiao-Wei ; Fang, Kang-Ling ; Li, Xi
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;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527027
