We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning.
- Authors
Ul Haq, Hafiz Burhan; Asif, Muhammad; Ahmad, Maaz Bin; Ashraf, Rehan; Mahmood, Toqeer
- Abstract
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.
- Subjects
DEEP learning; VIDEO summarization; VIDEO surveillance; DIGITAL video; FREE enterprise; VIDEOS; CELL phones
- Publication
Mathematical Problems in Engineering, 2022, p1
- ISSN
1024-123X
- Publication type
Article
- DOI
10.1155/2022/7453744