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Incremental tube construction for human action detection

Abstract:
Current state-of-the-art action detection systems are tailored for offline batch-processing applications. However, for online applications like human-robot interaction, current systems fall short. In this work, we introduce a real-time and online joint-labelling and association algorithm for action detection that can incrementally construct space-time action tubes on the most challenging untrimmed action videos in which different action categories occur concurrently. In contrast to previous methods, we solve the linking, action labelling and temporal localization problems jointly in a single pass. We demonstrate superior online association accuracy and speed (1.8ms per frame) as compared to the current state-of-the-art offline and online systems.
Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author


Publisher:
British Machine Vision Association
Host title:
29th British Machine Vision Conference (BMVC 2018)
Journal:
British Machine Vision Conference (BMVC 2018) More from this journal
Publication date:
2018-01-01
Acceptance date:
2018-07-06


Pubs id:
pubs:936522
UUID:
uuid:90a5ba63-6260-43e3-a1ae-3932b56540f5
Local pid:
pubs:936522
Source identifiers:
936522
Deposit date:
2018-11-02
ARK identifier:

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