Intelligent Video Highlights Generation with Front-Camera Emotion Sensing

Meyer, Hugo; Wei, Peter; Jiang, Xiaofan

doi:10.3390/s21041035

Cited by 4 publications

(3 citation statements)

References 64 publications

(95 reference statements)

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“…The average percentage of reconstruction (APOR) and the sparse border are used to manage the keyframe count in a greedy iterative technique for model optimization. A cloud-based system called HOMER was introduced by Meyer et al [41] for the creation of video highlights. With this technology, the user's emotions may be detected in order to provide a video summary.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Deep Learning Method for Video Summarization Based on the User Object of Interest

Haq,

Suwansantisuk,

Chamnongthai

2023

IJACSA

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Surveillance video is now able to play a vital role in maintaining security and protection thanks to the advancement of digital video technology. Businesses, both private and public, employ surveillance systems to monitor and track their daily operations. As a result, video generates a significant volume of data that needs to be further processed to satisfy security protocol requirements. Analyzing video requires a lot of effort and time, as well as quick equipment. The concept of a video summary was developed in order to overcome these limitations. To work past these limitations, the concept of video summarization has emerged. In this study, a deep learning-based method for customized video summarization is presented. This research enables users to produce a video summary in accordance with the User Object of Interest (UOoI), such as a car, airplane, person, bicycle, automobile, etc. Several experiments have been conducted on the two datasets, SumMe and self-created, to assess the efficiency of the proposed method. On SumMe and the self-created dataset, the overall accuracy is 98.7% and 97.5%, respectively, with a summarization rate of 93.5% and 67.3%. Furthermore, a comparison study is done to demonstrate that our proposed method is superior to other existing methods in terms of video summarization accuracy and robustness. Additionally, a graphic user interface is created to assist the user with summarizing the video using the UOoI.

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Section: Literature Reviewmentioning

confidence: 99%

An Optimized Deep Learning Method for Video Summarization Based on the User Object of Interest

Haq,

Suwansantisuk,

Chamnongthai

2023

IJACSA

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“…is method achieved a 43.9 precision rate. Meyer et al [54] presented a cloud-based system known as HOMER for the generation of video highlights. In this system, the video summary can be generated by detecting the user's emotions.…”

Section: Literature Reviewmentioning

confidence: 99%

An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning

Haq

Asif

Ahmad

et al. 2022

Mathematical Problems in Engineering

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The advancements in digital video technology have empowered video surveillance to play a vital role in ensuring security and safety. Public and private enterprises use surveillance systems to monitor and analyze daily activities. Consequently, a massive volume of data is generated in videos that require further processing to achieve security protocol. Analyzing video content is tedious and a time-consuming task. Moreover, it also requires high-speed computing hardware. The video summarization concept has emerged to overcome these limitations. This paper presents a customized video summarization framework based on deep learning. The proposed framework enables a user to summarize the videos according to the Object of Interest (OoI), for example, person, airplane, mobile phone, bike, and car. Various experiments are conducted to evaluate the performance of the proposed framework on the video summarization (VSUMM) dataset, title-based video summarization (TVSum) dataset, and own dataset. The accuracy of VSUMM, TVSum, and own dataset is 99.6%, 99.9%, and 99.2%, respectively. A desktop application is also developed to help the user summarize the video based on the OoI.

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“…Meyer, Wei and Jiang [ 27 ] presented “HOMER”, a cloud-based system for video highlight generation, which enables the automated, relevant, and flexible segmentation of videos. HOMER demonstrates an improvement of up to 38% in the

-score from baseline, while not requiring any external hardware.…”

mentioning

confidence: 99%