A new algorithm of infrared gait detection based on immune ant colony

Author

Jianhui Tan

Author_Institution

Fac. of Autom., Guangdong Univ. of Technol., Guangzhou, China

Volume

9

fYear

2011

fDate

12-14 Aug. 2011

Firstpage

4875

Lastpage

4878

Abstract

The accurate infrared gait detection is a research difficulty because of the unique thermal imaging characteristics. Therefore, the coarse result of infrared human detection is obtained by using the background subtraction method based on the single Gaussian background model, and is fine segmented by applying the improved pulse coupled neural network (PCNN). At the same time, the effect of the adaptive segmentation is reached by introducing the immune ant colony algorithm to fast determine the optimal segmentation parameters of PCNN. The simulation results show that this algorithm can achieve the ideal detection effect.

Keywords

Gaussian processes; evolutionary computation; gait analysis; image segmentation; infrared detectors; infrared imaging; neural nets; optimisation; PCNN; background subtraction method; immune ant colony; infrared gait detection; optimal segmentation parameters; pulse coupled neural network; single Gaussian background model; thermal imaging characteristics; Adaptation models; Approximation algorithms; Biological neural networks; Image segmentation; Immune system; Mathematical model; Neurons; Gait detection; Immune ant colony; Infrared; Pulse coupled neural network (PCNN); single gauss model;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on

Conference_Location

Harbin, Heilongjiang

Print_ISBN

978-1-61284-087-1

Type

conf

DOI

10.1109/EMEIT.2011.6024055

Filename

6024055