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Video Classification Using 3D Convolutional Neural Network

Video Classification Using 3D Convolutional Neural Network
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Author(s): K. Jairam Naik (National Institute of Technology, Raipur, India) and Annukriti Soni (National Institute of Technology, Raipur, India)
Copyright: 2021
Pages: 18
Source title: Advancements in Security and Privacy Initiatives for Multimedia Images
Source Author(s)/Editor(s): Ashwani Kumar (Vardhaman College of Engineering, India) and Seelam Sai Satyanarayana Reddy (Vardhaman College of Engineering, India)
DOI: 10.4018/978-1-7998-2795-5.ch001


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Since video includes both temporal and spatial features, it has become a fascinating classification problem. Each frame within a video holds important information called spatial information, as does the context of that frame relative to the frames before it in time called temporal information. Several methods have been invented for video classification, but each one is suffering from its own drawback. One of such method is called convolutional neural networks (CNN) model. It is a category of deep learning neural network model that can turn directly on the underdone inputs. However, such models are recently limited to handling two-dimensional inputs only. This chapter implements a three-dimensional convolutional neural networks (CNN) model for video classification to analyse the classification accuracy gained using the 3D CNN model. The 3D convolutional networks are preferred for video classification since they inherently apply convolutions in the 3D space.

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