An Optimized Deep Learning Method for Video Summarization Based on the User Object of Interest
Journal article
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Publication Details
Author list: Ul Haq H.B.; Suwansantisuk W.; Chamnongthai K.
Publisher: SAI Organization
Publication year: 2023
Journal: International Journal of Advanced Computer Science and Applications (2158-107X)
Volume number: 14
Issue number: 10
Start page: 244
End page: 256
Number of pages: 13
ISSN: 2158-107X
eISSN: 2156-5570
Languages: English-Great Britain (EN-GB)
Abstract
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. ฉ (2023), (Science and Information Organization). All Rights Reserved.
Keywords
SumMe, surveillance systems, user object of interest, Video summarization
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