A survey of recent work on video summarization: approaches and techniques

Tiwari, Vasudha; Bhatnagar, Charul

doi:10.1007/s11042-021-10977-y

Cited by 42 publications

(34 citation statements)

References 124 publications

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“…Such videos are often not fully annotated, but can be used for weakly supervised or unsupervised approaches. Some recent surveys provide lists of existing video datasets (Apostolidis et al, 2021;Tiwari and Bhatnagar, 2021).…”

Section: Datasetmentioning

confidence: 99%

Video Summarization Overview

Otani

Song

Wang

2022

FNT in Computer Graphics and Vision

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With the broad growth of video capturing devices and applications on the web, it is more demanding to provide desired video content for users efficiently. Video summarization facilitates quickly grasping video content by creating a compact summary of videos. Much effort has been devoted to automatic video summarization, and various problem settings and approaches have been proposed. Our goal is to provide an overview of this field. This survey covers early studies as well as recent approaches which take advantage of deep learning techniques. We describe video summarization approaches and their underlying concepts. We also discuss benchmarks and evaluations. We overview how prior work addressed evaluation and detail the pros and cons of the evaluation protocols. Last but not least, we discuss open challenges in this field.

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Section: Datasetmentioning

confidence: 99%

Video Summarization Overview

Otani

Song

Wang

2022

FNT in Computer Graphics and Vision

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“…A video comprises a series of video frames. If a video summary has a high recall, precision, and low redundancy rate, it is deemed to be good (Tiwari and Bhatnagar, 2021 ). When a video is played, each frame is presented in a specific order and at a specific frame rate.…”

Section: Introductionmentioning

confidence: 99%

From video summarization to real time video summarization in smart cities and beyond: A survey

Shambharkar

Goel

2023

Front. Big Data

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With the massive expansion of videos on the internet, searching through millions of them has become quite challenging. Smartphones, recording devices, and file sharing are all examples of ways to capture massive amounts of real time video. In smart cities, there are many surveillance cameras, which has created a massive volume of video data whose indexing, retrieval, and administration is a difficult problem. Exploring such results takes time and degrades the user experience. In this case, video summarization is extremely useful. Video summarization allows for the efficient storing, retrieval, and browsing of huge amounts of information from video without sacrificing key features. This article presents a classification and analysis of video summarization approaches, with a focus on real-time video summarization (RVS) domain techniques that can be used to summarize videos. The current study will be useful in integrating essential research findings and data for quick reference, laying the preliminaries, and investigating prospective research directions. A variety of practical uses, including aberrant detection in a video surveillance system, have made successful use of video summarization in smart cities.

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“…Dynamic summaries also referred to as video skim, are produced by video segments that analyse the audio and visual content of the video. A static summary or keyframe‐based summary is a grouping of the pertinent keyframes that are needed to produce the desired summary and are chosen in a sequential sequence (Tiwari & Bhatnagar, 2021). These can also be referred to as representative frames (R‐frames), or a group of standout images gleaned from the video data.…”

Section: Introductionmentioning

confidence: 99%

Auto encoder with mode‐based learning for keyframe extraction in video summarization

Shambharkar,

Goel

2023

Expert Systems

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The exponential increase in video consumption has created new difficulties for browsing and navigating through video more effectively and efficiently. Researchers are interested in video summarization because it offers a brief but instructive video version that helps users and systems save time and effort when looking for and comprehending relevant content. Key frame extraction is a method of video summarization that only chooses the most important frames from a given video. In this article, a novel supervised learning method ‘TC‐CLSTM Auto Encoder with Mode‐based Learning’ using temporal and spatial features is proposed for automatically choosing keyframes or important sub‐shots from videos. The method was able to achieve an average F‐score of 84.35 on TVSum dataset. Extensive tests on benchmark data sets show that the suggested methodology outperforms state‐of‐the‐art methods.

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A survey of recent work on video summarization: approaches and techniques

Cited by 42 publications

References 124 publications

Video Summarization Overview

Video Summarization Overview

From video summarization to real time video summarization in smart cities and beyond: A survey

Auto encoder with mode‐based learning for keyframe extraction in video summarization

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