Victoria Bloom

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In this paper a novel evaluation framework for measuring the performance of real-time action recognition methods is presented. The evaluation framework will extend the time-based event detection metric to model multiple distinct action classes. The proposed metric provides more accurate indications of the performance of action recognition algorithms for(More)
This paper presents a new, realistic and challenging human interaction dataset for multiplayer gaming, containing synchronised colour, depth and skeleton data. In contrast to existing datasets where the interactions are scripted, G3Di was captured using a novel gamesourcing method so the movements are more realistic. Our detection framework decomposes(More)
In this paper, a novel method is presented for lowlatency online action recognition from skeleton data. The introduction of pose based features has reduced viewpoint and anthropometric variations, so differing execution rates and personal styles are the major sources of classification error. Previous work for online action recognition fails to adequately(More)
Recognising human actions in real-time can provide users with a natural user interface (NUI) enabling a range of innovative and immersive applications. A NUI application should not restrict users’ movements; it should allow users to transition between actions in quick succession, which we term as compound actions. However, the majority of action recognition(More)