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Proceedings Paper

Detection and recognition of indoor smoking events
Author(s): Tse-Lun Bien; Chang Hong Lin
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Paper Abstract

Smoking in public indoor spaces has become prohibited in many countries since it not only affects the health of the people around you, but also increases the risk of fire outbreaks. This paper proposes a novel scheme to automatically detect and recognize smoking events by using exsiting surveillance cameras. The main idea of our proposed method is to detect human smoking events by recognizing their actions. In this scheme, the human pose estimation is introduced to analyze human actions from their poses. The human pose estimation method segments head and both hands from human body parts by using a skin color detection method. However, the skin color methods may fail in insufficient light conditions. Therefore, the lighting compensation is applied to help the skin color detection method become more accurate. Due to the human body parts may be covered by shadows, which may cause the human pose estimation to fail, the Kalman filter is applied to track the missed body parts. After that, we evaluate the probability features of hands approaching the head. The support vector machine (SVM) is applied to learn and recognize the smoking events by the probability features. To analysis the performance of proposed method, the datasets established in the survillance camera view under indoor enviroment are tested. The experimental results show the effectiveness of our proposed method with accuracy rate of 83.33%.

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 878424 (13 March 2013); doi: 10.1117/12.2020967
Show Author Affiliations
Tse-Lun Bien, National Taiwan Univ. of Science and Technology (Taiwan)
Chang Hong Lin, National Taiwan Univ. of Science and Technology (Taiwan)


Published in SPIE Proceedings Vol. 8784:
Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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