Open AccessDevelopment of framework for detecting smoking scene in video clips
Author(s) -
Poonam Ghuli,
B N Shashank,
Athri G Rao
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v13.i1.pp22-26
Subject(s) - clips , computer science , artificial intelligence , object detection , field (mathematics) , computer vision , software , object (grammar) , focus (optics) , cognitive neuroscience of visual object recognition , pattern recognition (psychology) , mathematics , operating system , physics , pure mathematics , optics
According to Global Adult Tobacco Survey 2016-17, 61.9% of people quitting tobacco the reason was the warnings displayed on the product covers. The focus of this paper is to automatically display warning messages in video clips. This paper explains the development of a system to automatically detect the smoking scenes using image recognition approach in video clips and then add the warning message to the viewer. The approach aims to detect the cigarette object using Tensorflow’s object detection API. Tensorflow is an open source software library for machine learning provided by Google which is broadly used in the field image recognition. At present, Faster R-CNN with Inception ResNet is theTensorflow’s slowest but most accurate model. Faster R-CNN with Inception Resnet v2 model is used to detect smoking scenes by training the model with cigarette as an object.