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A study on deep learning spatiotemporal models and feature extraction techniques for video understanding

A study on deep learning spatiotemporal models and feature extraction techniques for video... Video understanding requires abundant semantic information. Substantial progress has been made on deep learning models in the image, text, and audio domains, and notable efforts have been recently dedicated to the design of deep networks in the video domain. We discuss the state-of-the-art convolutional neural network (CNN) and its pipelines for the exploration of video features, various fusion strategies, and their performances; we also discuss the limitations of CNN for long-term motion cues and the use of sequential learning models such as long short-term memory to overcome these limitations. In addition, we address various multi-model approaches for extracting important cues and score fusion techniques from hybrid deep learning frameworks. Then, we highlight future plans in this domain, recent trends, and substantial challenges for video understanding. This survey’s objectives are to study the plethora of approaches that have been developed for solving video understanding problems, to comprehensively study spatiotemporal cues, to explore the various models that are available for solving these problems and to identify the most promising approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

A study on deep learning spatiotemporal models and feature extraction techniques for video understanding

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References (91)

Publisher
Springer Journals
Copyright
Copyright © Springer-Verlag London Ltd., part of Springer Nature 2020
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-019-00190-x
Publisher site
See Article on Publisher Site

Abstract

Video understanding requires abundant semantic information. Substantial progress has been made on deep learning models in the image, text, and audio domains, and notable efforts have been recently dedicated to the design of deep networks in the video domain. We discuss the state-of-the-art convolutional neural network (CNN) and its pipelines for the exploration of video features, various fusion strategies, and their performances; we also discuss the limitations of CNN for long-term motion cues and the use of sequential learning models such as long short-term memory to overcome these limitations. In addition, we address various multi-model approaches for extracting important cues and score fusion techniques from hybrid deep learning frameworks. Then, we highlight future plans in this domain, recent trends, and substantial challenges for video understanding. This survey’s objectives are to study the plethora of approaches that have been developed for solving video understanding problems, to comprehensively study spatiotemporal cues, to explore the various models that are available for solving these problems and to identify the most promising approaches.

Journal

International Journal of Multimedia Information Retrieval – Springer Journals

Published: Jun 24, 2020

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