Skin-Color based human tracking using a probabilistic noise model combined with neural network

Authors:
Jin Young Kim;Min-Gyu Song;Seung You Na;Seong-Joon Baek;Seung Ho Choi;Joohun Lee
Affiliations:
Dept. of Electronics Eng., Chonnam National University, Gwangjoo, South Korea;Dept. of Electronics Eng., Chonnam National University, Gwangjoo, South Korea;Dept. of Electronics Eng., Chonnam National University, Gwangjoo, South Korea;Dept. of Electronics Eng., Chonnam National University, Gwangjoo, South Korea;Dept. of Multimedia Eng., Dongshin University, Chollanamdo, South Korea;Dept. of Internet Broadcasting Dong-A Broadcasting College, Ansung, South Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 3
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Abstract

We develop a simple and fast human tracking system based on skin-color using Kalman filter for humanoid robots. For our human tracking system we propose a fuzzy and probabilistic model of observation noise, which is important in Kalman filter implementation. The uncertainty of the observed candidate region is estimated by neural network. Neural network is also used for the verification of face-like regions obtained from skin-color information. Then the probability of observation noise is controlled based on the uncertainty value of the observation. Through the real-human tracking experiments we compare the performance of the proposed model with the conventional Gaussian noise model. The experimental results show that the proposed model enhances the tracking performance and also can compensate the biased estimations of the baseline system.