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Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection
KTH. Polytechnic University of Catalonia, Spain.
KTH, Skolan för datavetenskap och kommunikation (CSC).
Vise andre og tillknytning
2017 (engelsk)Inngår i: 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017, IEEE Computer Society, 2017, Vol. 2017, s. 2153-2160Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Despite significant progress in the development of human action detection datasets and algorithms, no current dataset is representative of real-world aerial view scenarios. We present Okutama-Action, a new video dataset for aerial view concurrent human action detection. It consists of 43 minute-long fully-annotated sequences with 12 action classes. Okutama-Action features many challenges missing in current datasets, including dynamic transition of actions, significant changes in scale and aspect ratio, abrupt camera movement, as well as multi-labeled actors. As a result, our dataset is more challenging than existing ones, and will help push the field forward to enable real-world applications.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2017. Vol. 2017, s. 2153-2160
Serie
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508 ; 2017
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-215882DOI: 10.1109/CVPRW.2017.267ISI: 000426448300260Scopus ID: 2-s2.0-85030250941ISBN: 9781538607336 (tryckt)OAI: oai:DiVA.org:kth-215882DiVA, id: diva2:1149853
Konferanse
30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017, Honolulu, United States, 21 July 2017 through 26 July 2017
Merknad

QC 20171017

Tilgjengelig fra: 2017-10-17 Laget: 2017-10-17 Sist oppdatert: 2018-03-22bibliografisk kontrollert

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