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DOI: 10.14569/IJACSA.2024.0151181
PDF

DSTC-Sum: A Supervised Video Summarization Model Using Depthwise Separable Temporal Convolutional

Author 1: M. Hamza Eissa
Author 2: Hesham Farouk
Author 3: Kamal Eldahshan
Author 4: Amr Abozeid

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 11, 2024.

  • Abstract and Keywords
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Abstract: The exponential growth in video content has created a critical need for efficient video summarization techniques to enable faster and more accurate information retrieval. Video summarization has excellent potential to simplify the analysis of large video databases in various application areas ranging from surveillance, education, entertainment, and research. DSTC-Sum, a novel supervised video summarization model, is proposed based on Depthwise Separable Temporal Convolutional (DSTC). Leveraging the superior representational efficiency of DSTCN, the model addresses computational challenges and training inefficiencies encountered in traditional recurrent architectures such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs). Additionally, this approach reduces computational overhead and memory usage. DSTC-Sum achieved state-of-the-art performance on two commonly used benchmark datasets, TVSum and SumMe, and outperformed all previous methods with F-scores by 1.8% and 3.33%, respectively. To validate the model's generality and robustness, the model was further tested on the YouTube and Open Video Project (OVP) datasets. The proposed model did slightly better on these datasets than several popular techniques, with F scores of 60.3 and 58.5, respectively. Finally, these findings confirm that this model captures long-term temporal dependencies and produces high-quality video summaries across all types of videos.

Keywords: Video summarization; depthwise separable temporal convolutional; video processing; deep learning

M. Hamza Eissa, Hesham Farouk, Kamal Eldahshan and Amr Abozeid, “DSTC-Sum: A Supervised Video Summarization Model Using Depthwise Separable Temporal Convolutional” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151181

@article{Eissa2024,
title = {DSTC-Sum: A Supervised Video Summarization Model Using Depthwise Separable Temporal Convolutional},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151181},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151181},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {M. Hamza Eissa and Hesham Farouk and Kamal Eldahshan and Amr Abozeid}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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