Next, based on color characteristics, this study identifies human skin color areas along with the candidate areas of nipples, one of the human body parts representing harmfulness.Finally, the method removes nonnipple areas among the detected candidate areas using the artificial neural network.
But, I only saw 1 blogger who tagged Tim with regards to Josh Moore.
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For detection of child unsafe content and its promoters, we perform two approaches, one based on supervised classification which uses an extensive set of video-level, user-level and comment-level features and another based Convolutional Neural Network using video frames.
Detection accuracy of 85.7% is achieved which can be leveraged to build a system to provide a safe You Tube experience for kids.
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Normally, a day after a Tim Tales scene has been released, there should already be a handful of bloggers who tagged Tim Kruger on Twitter about their latest scene.
Through detailed characterization studies, we are able to successfully conclude that unsafe content promoters are less popular and engage less as compared with other users.
Finally, using a network of unsafe content promoters and other users based on their engagements (likes, subscription and playlist addition) and other factors, we find that unsafe content is present very close to safe content and unsafe content promoters form very close knit communities with other users, thereby further increasing the likelihood of a child getting getting exposed to unsafe content.
Shot duration and camera motion, are the temporal domain features, and skin detection and color histogram are the spatial domain ones.
Using two data sets of 7 and 15 hours of video material, our experiments comparing two different SVM classifiers achieved an accuracy of 94.44%.
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