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2023 | OriginalPaper | Chapter

Video Content Analysis Using Deep Learning Methods

Authors : Gara Kiran Kumar, Athota Kavitha

Published in: Intelligent Systems and Machine Learning

Publisher: Springer Nature Switzerland

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Abstract

With the emergence of low-cost video recording devices, the internet is flooded with videos. However, most videos are uncategorized, necessitating video content analysis. This review effort addresses visual big data feature extraction, video segmentation, classification, and abstract video challenges. Exploring compressive sensing, deep learning (DL), and kernel methods for various tasks in video content analysis include video classification, clustering, dimension reduction, event detection, and activity recognition. DL is used to examine video footage recognition and classification. This study examines the algorithms’ flaws and benefits when applied to datasets. The classification approaches used Naive Bayes, support vector machine (SVM), and Deep Convolution Neural Network (DCNN) with Deer Hunting Optimization (DHO). Other approaches have higher false discovery and alarm rates than the DCNNDHO algorithm.

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Title: Video Content Analysis Using Deep Learning Methods
Authors: Gara Kiran Kumar
Athota Kavitha
Publisher: Springer Nature Switzerland
Book: Intelligent Systems and Machine Learning
Print ISBN: 978-3-031-35080-1

Electronic ISBN: 978-3-031-35081-8

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-35081-8_18

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