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Video Classification Using 3D Convolutional Neural Network

Video Classification Using 3D Convolutional Neural Network

K. Jairam Naik, Annukriti Soni
Copyright: © 2021 |Pages: 18
ISBN13: 9781799827955|ISBN10: 179982795X|ISBN13 Softcover: 9781799827962|EISBN13: 9781799827979
DOI: 10.4018/978-1-7998-2795-5.ch001
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MLA

Naik, K. Jairam, and Annukriti Soni. "Video Classification Using 3D Convolutional Neural Network." Advancements in Security and Privacy Initiatives for Multimedia Images, edited by Ashwani Kumar and Seelam Sai Satyanarayana Reddy, IGI Global, 2021, pp. 1-18. https://doi.org/10.4018/978-1-7998-2795-5.ch001

APA

Naik, K. J. & Soni, A. (2021). Video Classification Using 3D Convolutional Neural Network. In A. Kumar & S. Reddy (Eds.), Advancements in Security and Privacy Initiatives for Multimedia Images (pp. 1-18). IGI Global. https://doi.org/10.4018/978-1-7998-2795-5.ch001

Chicago

Naik, K. Jairam, and Annukriti Soni. "Video Classification Using 3D Convolutional Neural Network." In Advancements in Security and Privacy Initiatives for Multimedia Images, edited by Ashwani Kumar and Seelam Sai Satyanarayana Reddy, 1-18. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2795-5.ch001

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Abstract

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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