Human smoking event detection using visual interaction clues

Pin Wu*, Jun-Wei Hsieh, Jiun Cheng Cheng, Shyi Chyi Cheng, Shau Yin Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

29 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages4344-4347
Number of pages4
DOIs
StatePublished - 18 Nov 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 23 Aug 201026 Aug 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period23/08/1026/08/10

Fingerprint Dive into the research topics of 'Human smoking event detection using visual interaction clues'. Together they form a unique fingerprint.

Cite this