mPuff: automated detection of cigarette smoking puffs from respiration measurements
Proceedings of the 11th international conference on Information Processing in Sensor Networks
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
When do you light a fire?: capturing tobacco use with situated, wearable sensors
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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This paper presents a novel scheme to automatically and directly detect smoking events in video. In this scheme, a color-based ratio histogram analysis is introduced to extract the visual clues from appearance interactions between lighted cigarette and its human holder. The techniques of color re-projection and Gaussian Mixture Models (GMMs) enable the tasks of cigarette segmentation and tracking over the background pixels. Then, a key problem for event analysis is the non-regular form of smoking events. Thus, we propose a self-determined mechanism to analyze this suspicious event using HHM framework. Due to the uncertainties of cigarette size and color, there is no automatic system which can well analyze human smoking events directly from videos. The proposed scheme is compatible to detect the smoking events of uncertain actions with various cigarette sizes, colors, and shapes, and has capacity to extend visual analysis to human events of similar interaction relationship. Experimental results show the effectiveness and real-time performances of our scheme in smoking event analysis.