Pixel-wise ground truth annotation in videos: An semi-automatic approach for pixel-wise and semantic object annotation
Autor(en): | Schöning, J. Faion, P. Heidemann, G. |
Herausgeber: | De Marsico, M. di Baja, G.S. Fred, A. |
Stichwörter: | Automation; Ground truth; Image segmentation; Knowledge extraction from video data; Pattern recognition; Pixels; Polygon shaped; Semantic ground truth annotation; Semantic relations; Semantics; Semi-automatic; Semi-automatic segmentation; Semi-automatics; Video annotation; Video annotations, Three dimensional computer graphics; Video recording, Geometric primitives | Erscheinungsdatum: | 2016 | Herausgeber: | SciTePress | Journal: | ICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods | Startseite: | 690 | Seitenende: | 697 | Zusammenfassung: | In the last decades, a large diversity of automatic, semi-automatic and manual approaches for video segmentation and knowledge extraction from video-data has been proposed. Due to the high complexity in both the spatial and temporal domain, it continues to be a challenging research area. In order to develop, train, and evaluate new algorithms, ground truth of video-data is crucial. Pixel-wise annotation of ground truth is usually time-consuming, does not contain semantic relations between objects and uses only simple geometric primitives. We provide a brief review of related tools for video annotation, and introduce our novel interactive and semi-automatic segmentation tool iSeg. Extending an earlier implementation, we improved iSeg with a semantic time line, multithreading and the use of ORB features. A performance evaluation of iSeg on four data sets is presented. Finally, we discuss possible opportunities and applications of semantic polygon-shaped video annotation, such as 3D reconstruction and video inpainting. © Copyright 2016 by SCITEPRESS -Science and Technology Publications, Lda. All rights reserved. |
Beschreibung: | Conference of 5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016 ; Conference Date: 24 February 2016 Through 26 February 2016; Conference Code:119680 |
ISBN: | 9789897581731 | DOI: | 10.5220/0005823306900697 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969919958&doi=10.5220%2f0005823306900697&partnerID=40&md5=2a17c39b74238caac094728460c961e6 |
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