Abstract—Detection performance of the smoke in the outdoor depends on weather conditions, the shadow by the movement of the sun, or illumination changes, etc. In this paper, a smoke detection system in conjunction with a robust background estimate algorithm to environment change in the outdoor in daytime is proposed. Gaussian Mixture Model (GMM) is applied as background estimation algorithm, and also, Adaboost algorithm is applied to detect the smoke for candidate region. Through the experiments with input videos obtained from a various weather conditions at the same actual road, the proposed algorithms were useful to detect smoke in the outdoor.

Index Terms—GMM, adaboost, smoke detection, outdoor environments.

Hyuntae Kim is with Department of Multimedia Engineering, Dongeui University, Busan, 614-714 South Korea (e-mail: htaekim@deu.ac.kr).
Daehyun Ryu is with Faculty of Information Technology, Hansei University, Kyunggi Province, South Korea (e-mail:dhryu@hansei.ac.kr).
Jangsik Park is with Department of Electronics Engineering, Kyungsung University, Busan, 608-736 South Korea (e-mail: jsipark@ks.ac.kr).

Cite:Hyuntae Kim, Daehyun Ryu, and Jangsik Park, "Smoke Detection Using GMM and Adaboost," International Journal of Computer and Communication Engineering vol. 3, no. 2, pp. 123-126, 2014.