ICSES Transactions on Data Science, Engineering and Technology
Vol. 1, No. 2, Aug. 2018
Automatic Semantic Video Annotation
a Saveetha University, Chennai, India
Highlights and Novelties
1. The importance of Video Analysis in low-level , middle level and high level is specified.
2. An overview on Video Annotation Techniques such as manual annotation, rule based and machine learning is presented.
3. The purpose of Video Annotation Tools such as Advene, SVAT and VideoAnnex is highlighted.
4. The necessity for Semantic Video Summarization due to semantic gap and other issues is discussed.
The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labeling and annotation, are used to represent appropriate semantics for search and retrieval. The semantics should be inspired by the human cognitive way of perceiving to describe videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the ‘semantic gap’. Tackling this gap is harder in the case of unconstrained videos due to lack of semantics knowledge.
Video based applications such as video surveillance, road trafﬁc control, sports events detection require a strong human intervention when a semantic understanding of contents is needed to detect objects, actions or events within a video stream. Manual analysis of video sequences is a very time consuming task and it often leads to inaccurate results due to the "video blindness". In the video surveillance domain, for example, it has been stimulated that an operator can miss up to 95% of scene activities after only 22 minutes of analysis.
In the last years, great efforts by the computer vision research community leads to the development of robust and reliable algorithms for video analysis tasks at different levels: 1) Low-level video analysis methods address the ability to ﬁnd the image regions corresponding to objects of interest (detection) and then track them across different frames while maintaining the correct identities (tracking). 2) Mid-level video analysis methods face the problem of recognizing simple or “atomic” events or activities. 3)High-level video analysis methods concentrate on the detection of “complex” events or activities
Video Analysis Video Annotation Techniques Video Annotation Tools Semantic Video Annotation
Copyright and Licence
© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors. This manuscript is published in Open-Access manner based on the copyright licence of Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0).
Cite this manuscript as
Kalaivani Anbarasan, "Automatic Semantic Video Annotation ," ICSES Transactions on Data Science, Engineering and Technology, vol. 1, no. 2, pp. 1-2, Aug. 2018.
For External Scientific Databeses
title="Automatic Semantic Video Annotation ",
journal="ICSES Transactions on Data Science, Engineering and Technology (ITDSET)",
publisher= "International Computer Science and Engineering Society (ICSES)",
%0 Journal Article
%T Automatic Semantic Video Annotation
%A Kalaivani Anbarasan
%J ICSES Transactions on Data Science, Engineering and Technology (ITDSET)
%I International Computer Science and Engineering Society (ICSES)
< name="citation_title" content="Automatic Semantic Video Annotation ">
< name="citation_author" content="Kalaivani Anbarasan">
< name="citation_publication_date" content="2018-08-30">
< name="citation_journal_title" content="ICSES Transactions on Data Science, Engineering and Technology (ITDSET)">
< name="citation_issn" content="2467-297X">
< name="citation_volume" content="1">
< name="citation_issue" content="2">
< name="citation_firstpage" content="1">
< name="citation_lastpage" content="2">
< name="citation_pdf_url" content="http://www.i-cses.com/files/download.php?pID=149">