New Scheme for Smoker Detection 


Vol. 41,  No. 9, pp. 1120-1131, Sep.  2016


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  Abstract

In this paper, we propose a smoker recognition algorithm, detecting smokers in a video sequence in order to prevent fire accidents. We use description-based method in hierarchical approaches to recognize smoker"s activity, the algorithm consists of background subtraction, object detection, event search, event judgement. Background subtraction generates slow-motion and fast-motion foreground image from input image using Gaussian mixture model with two different learning-rate. Then, it extracts object locations in the slow-motion image using chain-rule based contour detection. For each object, face is detected by using Haar-like feature and smoke is detected by reflecting frequency and direction of smoke in fast-motion foreground. Hand movements are detected by motion estimation. The algorithm examines the features in a certain interval and infers that whether the object is a smoker. It robustly can detect a smoker among different objects while achieving real-time performance.

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  Cite this article

[IEEE Style]

J. Lee, H. Lee, D. Lee, S. Oh, "New Scheme for Smoker Detection," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 9, pp. 1120-1131, 2016. DOI: .

[ACM Style]

Jong-seok Lee, Hyun-jae Lee, Dong-kyu Lee, and Seoung-jun Oh. 2016. New Scheme for Smoker Detection. The Journal of Korean Institute of Communications and Information Sciences, 41, 9, (2016), 1120-1131. DOI: .

[KICS Style]

Jong-seok Lee, Hyun-jae Lee, Dong-kyu Lee, Seoung-jun Oh, "New Scheme for Smoker Detection," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 9, pp. 1120-1131, 9. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880