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V2F: real time video segmentation with Apache Flink

Kastrinakis Dimitrios, Petrakis Evripidis

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URIhttp://purl.tuc.gr/dl/dias/CA480050-A068-47EC-8B2F-3F16E7F3327A-
Identifierhttps://doi.org/10.1007/978-3-031-20716-7_12-
Identifierhttps://link.springer.com/chapter/10.1007/978-3-031-20716-7_12-
Languageen-
Extent12 pagesen
TitleV2F: real time video segmentation with Apache Flinken
CreatorKastrinakis Dimitriosen
CreatorΚαστρινακης Δημητριοςel
CreatorPetrakis Evripidisen
CreatorΠετρακης Ευριπιδηςel
PublisherSpringeren
DescriptionWe are grateful to Google for the Google Cloud Platform Education Grants program. The work has received funding from the European Union’s Horizon 2020 - Research and Innovation Framework Programme H2020-SU-SEC-2019, under Grant Agreement No 883272- BorderUAS.en
Content SummaryV2F is a distributed video processing system for bounded (i.e., stored) and unbounded (i.e., continuous) or real time video streams. Apache Flink applies a series of operators in a pipeline to transform a video stream into shots. These operators are replicated to work in parallel on Flink-managed computing nodes. The V2F deployment of the standard twin-comparison video segmentation method is more than 7 times faster than its non-parallel (i.e., sequential) implementation.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2024-11-26-
Date of Publication2022-
SubjectVideo segmentationen
SubjectTwin comparisonen
SubjectApache Flinken
Bibliographic CitationD. Kastrinakis and E. G. M. Petrakis, “V2F: real time video segmentation with Apache Flink,” in Advances in Visual Computing, vol 13599, Lecture Notes in Computer Science, G. Bebis, B. Li, A. Yao, Y. Liu, Y. Duan, M. Lau, R. Khadka, A. Crisan, R. Chang, Eds., Cham, Switzerland: Springer, 2022, pp. 153–164, doi: 10.1007/978-3-031-20716-7_12.en

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