A video summarization framework based on activity attention modeling using deep features for smart campus surveillance system.

Autor:	Muhammad W; Center of Excellence in Information Technology, Institute of Management Sciences (IMSciences), Peshawar, Peshawar, KPK, Pakistan., Ahmed I; Center of Excellence in Information Technology, Institute of Management Sciences (IMSciences), Peshawar, Peshawar, KPK, Pakistan., Ahmad J; Center of Excellence in Information Technology, Institute of Management Sciences (IMSciences), Peshawar, Peshawar, KPK, Pakistan.; Department of Computer Science, Islamia College Peshawar (Chartered University), Peshawar, Pakistan., Nawaz M; Center of Excellence in Information Technology, Institute of Management Sciences (IMSciences), Peshawar, Peshawar, KPK, Pakistan., Alabdulkreem E; Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia., Ghadi Y; Department of Computer Science, Al Ain University, Al Ain, UAE.
Jazyk:	angličtina
Zdroj:	PeerJ. Computer science [PeerJ Comput Sci] 2022 Mar 25; Vol. 8, pp. e911. Date of Electronic Publication: 2022 Mar 25 (Print Publication: 2022).
DOI:	10.7717/peerj-cs.911
Abstrakt:	Like other business domains, digital monitoring has now become an integral part of almost every academic institution. These surveillance systems cover all the routine activities happening on the campus while producing a massive volume of video data. Selection and searching the desired video segment in such a vast video repository is highly time-consuming. Effective video summarization methods are thus needed for fast navigation and retrieval of video content. This paper introduces a keyframe extraction method to summarize academic activities to produce a short representation of the target video while preserving all the essential activities present in the original video. First, we perform fine-grain activity recognition using a realistic Campus Activities Dataset (CAD) by modeling activity attention scores using a deep CNN model. In the second phase, we use the generated attention scores for each activity category to extract significant video frames. Finally, we evaluate the inter-frame similarity index used to reduce the number of redundant frames and extract only the representative keyframes. The proposed framework is tested on different videos, and the experimental results show the performance of the proposed summarization process. Competing Interests: Imran Ahmed is an Academic Editor for PeerJ. (© 2022 Muhammad et al.)
Databáze:	MEDLINE
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