International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 02 | Feb 2024
p-ISSN: 2395-0072
www.irjet.net
Exploring Various Techniques for Video Summarization Ajinkya Somawanshi, Devang Shirodkar, Vinayak Yadav, Krushna Tawri, Prof. Rakhi Punwatkar 1234 UG Student, Dept. of computer Engineering, Zeal college of engineering, Maharashtra, India 5Professor, Dept. of computer Engineering, Zeal college of engineering, Maharashtra, India
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Abstract - Video summarization is a critical task in
lengthy videos into concise representations while preserving key information. The primary objective of VS is to analyze videos by removing unnecessary frames and preserving keyframes, thus facilitating efficient browsing and structured access to video content. Automatic VS (AVS) powered by Artificial Intelligence (AI) is a rapidly growing research area, enabling the automatic summarization of lengthy videos without human intervention.
multimedia analysis, especially in today's digital world, where the volume of video data is vast. Deep learning methods have been widely explored for this purpose, but they often suffer from inefficiencies in processing longduration videos. This paper addresses the challenge of unsupervised video summarization by proposing a novel approach that selects a sparse subset of video frames to optimally represent the input video. The key idea is to train a deep summarizer network using a generative adversarial framework, comprising an autoencoder LSTM network as the summarizer and another LSTM network as the discriminator. The summarizer LSTM is trained to select video frames and decode the obtained summarization to reconstruct the input video. At the same time, the discriminator LSTM aims to distinguish between the original video and its reconstruction. The adversarial training between the summarizer and discriminator, along with regularization for sparsity, enables the network to learn to generate optimal video summaries without the need for labeled data. Evaluation of multiple benchmark datasets demonstrates competitive performance compared to fully supervised state-of-the-art approaches, showcasing the effectiveness of the proposed method in unsupervised video summarization.
The applications of VS span various domains, including surveillance, education, entertainment, and medical diagnostics. From monitoring and tracking to creating movie trailers and enabling video search engines, the practical use cases of video summaries are diverse and farreaching. Additionally, VS plays a vital role in reducing frame redundancy, thereby optimizing storage requirements and computational time. This paper focuses on the problem of unsupervised video summarization, where the goal is to select a sparse subset of frames that minimizes the representation error between the original video and its summary. We propose a novel approach based on a generative adversarial framework, combining an autoencoder LSTM network as the summarizer and another LSTM network as the discriminator. By training these networks adversarially, we aim to produce optimal video summarizations without the need for labeled data.
Key Words: Event summarization · Critical information in videos · Surveillance systems · Video analysis · Multimedia analysis · Deep learning · Unsupervised learning · Autoencoder LSTM · Long short-term memory network (LSTM)
In this paper, we present an overview of our proposed approach to unsupervised video summarization and discuss its application in various domains. We also delve into the technical details of our methodology, including the use of deep learning architectures such as CNNs and LSTMs for feature extraction and the implementation of a generative adversarial network for optimization. Through experimental evaluation of benchmark datasets, we demonstrate the effectiveness of our approach in generating high-quality video summaries.
1. INTRODUCTION In today's digital age, videos have become one of the most influential and prevalent forms of multimedia, connecting with users quickly and effectively. The widespread availability of high-speed internet and affordable storage has led to an explosion of video data generation, with platforms like YouTube, Netflix, and social media hosting vast amounts of visual content. However, this abundance of video data presents challenges in terms of storage, bandwidth, and human resources required for analysis.
Overall, this paper contributes to the ongoing research in video summarization by presenting a novel unsupervised approach that leverages deep learning and generative adversarial techniques to produce compact and informative video summaries across diverse domains.
Video summarization (VS) has emerged as a crucial technique to address these challenges by condensing
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