In today’s society, videos thrive along with the risk of spreading violent content. Preventing this risk through automated software is becoming increasingly crucial to reduce human efforts. This research offers a video-level solution for detecting scenes of violence in videos.
Computer Vision is an interdisciplinary field born in the late 1960s, whose purpose is to reproduce human visual systems through methods of acquiring, processing, analyzing, and understanding digital images.
In the last few years, deep learning models have achieved outstanding results in various number of tasks such as object recognition and machine translation.
Convolutional Neural Network gained lots of success in 2012 thanks to AlexNet, a Neural Network capable of excellent performance on the ImageNet dataset. Since then numerous variants of CNNs have been developed, pushing the limits of the architecture for image classification.
This thesis delves into the recent developments of reinforcement learning methods, with a particular focus on industrial applications.
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