Video fingerprinting: Past, present, and future
The last decades have seen video production and consumption rise significantly: TV/cinematography, social networking, digital marketing, and video surveillance incrementally and cumulatively turned video content into the predilection type of data to be exchanged, stored, and processed. Belonging to...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Signal Processing |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsip.2022.984169/full |
| _version_ | 1817991949597016064 |
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| author | Mohamed Allouche Mohamed Allouche Mihai Mitrea |
| author_facet | Mohamed Allouche Mohamed Allouche Mihai Mitrea |
| author_sort | Mohamed Allouche |
| collection | DOAJ |
| description | The last decades have seen video production and consumption rise significantly: TV/cinematography, social networking, digital marketing, and video surveillance incrementally and cumulatively turned video content into the predilection type of data to be exchanged, stored, and processed. Belonging to video processing realm, video fingerprinting (also referred to as content-based copy detection or near duplicate detection) regroups research efforts devoted to identifying duplicated and/or replicated versions of a given video sequence (query) in a reference video dataset. The present paper reports on a state-of-the-art study on the past and present of video fingerprinting, while attempting to identify trends for its development. First, the conceptual basis and evaluation frameworks are set. This way, the methodological approaches (situated at the cross-roads of image processing, machine learning, and neural networks) can be structured and discussed. Finally, fingerprinting is confronted to the challenges raised by the emerging video applications (e.g., unmanned vehicles or fake news) and to the constraints they set in terms of content traceability and computational complexity. The relationship with other technologies for content tracking (e.g., DLT - Distributed Ledger Technologies) are also presented and discussed. |
| first_indexed | 2024-04-14T01:20:00Z |
| format | Article |
| id | doaj.art-c310c4aff1d24fab902d8b555c4b9494 |
| institution | Directory Open Access Journal |
| issn | 2673-8198 |
| language | English |
| last_indexed | 2024-04-14T01:20:00Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Signal Processing |
| spelling | doaj.art-c310c4aff1d24fab902d8b555c4b94942022-12-22T02:20:42ZengFrontiers Media S.A.Frontiers in Signal Processing2673-81982022-09-01210.3389/frsip.2022.984169984169Video fingerprinting: Past, present, and futureMohamed Allouche0Mohamed Allouche1Mihai Mitrea2Telecom SudParis, ARTEMIS Department, SAMOVAR Laboratory, Evry, FranceVIDMIZER, Paris, FranceTelecom SudParis, ARTEMIS Department, SAMOVAR Laboratory, Evry, FranceThe last decades have seen video production and consumption rise significantly: TV/cinematography, social networking, digital marketing, and video surveillance incrementally and cumulatively turned video content into the predilection type of data to be exchanged, stored, and processed. Belonging to video processing realm, video fingerprinting (also referred to as content-based copy detection or near duplicate detection) regroups research efforts devoted to identifying duplicated and/or replicated versions of a given video sequence (query) in a reference video dataset. The present paper reports on a state-of-the-art study on the past and present of video fingerprinting, while attempting to identify trends for its development. First, the conceptual basis and evaluation frameworks are set. This way, the methodological approaches (situated at the cross-roads of image processing, machine learning, and neural networks) can be structured and discussed. Finally, fingerprinting is confronted to the challenges raised by the emerging video applications (e.g., unmanned vehicles or fake news) and to the constraints they set in terms of content traceability and computational complexity. The relationship with other technologies for content tracking (e.g., DLT - Distributed Ledger Technologies) are also presented and discussed.https://www.frontiersin.org/articles/10.3389/frsip.2022.984169/fullVideo fingerprintingreviewmachine learingneural networkvisual featureDLT (distributed ledger technologies) |
| spellingShingle | Mohamed Allouche Mohamed Allouche Mihai Mitrea Video fingerprinting: Past, present, and future Frontiers in Signal Processing Video fingerprinting review machine learing neural network visual feature DLT (distributed ledger technologies) |
| title | Video fingerprinting: Past, present, and future |
| title_full | Video fingerprinting: Past, present, and future |
| title_fullStr | Video fingerprinting: Past, present, and future |
| title_full_unstemmed | Video fingerprinting: Past, present, and future |
| title_short | Video fingerprinting: Past, present, and future |
| title_sort | video fingerprinting past present and future |
| topic | Video fingerprinting review machine learing neural network visual feature DLT (distributed ledger technologies) |
| url | https://www.frontiersin.org/articles/10.3389/frsip.2022.984169/full |
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