People across various fields rely on automated video summarization
tools to manage extensive video content efficiently. This research
focuses on developing a dynamic, user-centered approach to video
summarization, accommodating both patient and impatient user needs.
The system aims to handle lengthy videos by identifying and cataloging
all objects within them. It follows a three-step process: Object-of-
Interest selection, object detection/localization, and video
summarization. For patient viewers, it offers comprehensive scene
identification and storage. For impatient users, it provides concise
summaries quickly. By adapting itself to individual preferences, this
research will make videos more accessible and useful by providing
personalized video summaries which will help avoid information
overload in various spheres such as security, entertainment, or
personal documentation. This research used deep learning models like
YOLOv8, ResNeXt as well as LSTM to implement this user- centric
approach to video summarization.
K.R. Sarath Chandran, Adithi Shankar, Geethapriya Thandavamurthi
Sri Sivasubramaniya Nadar College of Engineering, India
OOI, Patient User, Impatient User, YOLOv8, ResNeXt, LSTM, RNN, CNN
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 4 , Pages: 3553 - 3562 )
Date of Publication :
May 2025
Page Views :
31
Full Text Views :
8
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