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http://dx.doi.org/10.9708/jksci.2012.17.7.045

Crowd Density Estimation with Multi-class Adaboost in elevator  

Kim, Dae-Hun (Dept. of Electrical Engineering, Korea University)
Lee, Young-Hyun (Dept. of Visual Information Processing, Korea University)
Ku, Bon-Hwa (Dept. of Visual Information Processing, Korea University)
Ko, Han-Seok (Dept. of Electrical Engineering, Korea University)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.7, 2012 , pp. 45-52 More about this Journal
Abstract
In this paper, an crowd density in elevator estimation method based on multi-class Adaboost classifier is proposed. The SOM (Self-Organizing Map) based conventional methods have shown insufficient performance in practical scenarios and have weakness for low reproducibility. The proposed method estimates the crowd density using multi-class Adaboost classifier with texture features, namely, GLDM(Grey-Level Dependency Matrix) or GGDM(Grey-Gradient Dependency Matrix). In order to classify into multi-label, weak classifier which have better performance is generated by modifying a weight update equation of general Adaboost algorithm. The crowd density is classified into four categories depending on the number of persons in the crowd, which can be 0 person, 1-2 people, 3-4 people, and 5 or more people. The experimental results under indoor environment show the proposed method improves detection rate by about 20% compared to that of the conventional method.
Keywords
multi-class Adaboost; crowd density estimation; texture feature;
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