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Effective Video Summarization Approach Based on Visual Attention

Hilal Ahmad1, Habib Ullah Khan2, Sikandar Ali3,*, Syed Ijaz Ur Rahman1, Fazli Wahid3, Hizbullah Khattak4

1 Department of Computer Sciences, Islamia College Peshawar Khyber, Pakhtunkhwa, Pakistan
2 Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, 2713, Qatar
3 Department of Information Technology, The University of Haripur, Khyber Pakhtunkhwa, Pakistan
4 Department of Information Technology, Hazara University Mansehra, Khyber Pakhtunkhwa, Pakistan

* Corresponding Author: Sikandar Ali. Email: email

(This article belongs to this Special Issue: Application of Machine-Learning in Computer Vision)

Computers, Materials & Continua 2022, 71(1), 1427-1442. https://doi.org/10.32604/cmc.2022.021158

Abstract

Video summarization is applied to reduce redundancy and develop a concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes, the frames that stand out visually are extracted as key frames based on human attention modeling theories. The schemes for modeling visual attention have proven to be effective for video summaries. Nevertheless, the high cost of computing in such techniques restricts their usability in everyday situations. In this context, we propose a method based on KFE (key frame extraction) technique, which is recommended based on an efficient and accurate visual attention model. The calculation effort is minimized by utilizing dynamic visual highlighting based on the temporal gradient instead of the traditional optical flow techniques. In addition, an efficient technique using a discrete cosine transformation is utilized for the static visual salience. The dynamic and static visual attention metrics are merged by means of a non-linear weighted fusion technique. Results of the system are compared with some existing state-of-the-art techniques for the betterment of accuracy. The experimental results of our proposed model indicate the efficiency and high standard in terms of the key frames extraction as output.

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Cite This Article

H. Ahmad, H. Ullah Khan, S. Ali, S. Ijaz Ur Rahman, F. Wahid et al., "Effective video summarization approach based on visual attention," Computers, Materials & Continua, vol. 71, no.1, pp. 1427–1442, 2022. https://doi.org/10.32604/cmc.2022.021158



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