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- W2493021729 abstract "Visual categorisation of human motion in video clips has been an active field of research in recent years. However, most published methods either analyse an entire video and assign it a single category label, or use relatively large look-ahead to classify each frame. Contrary to these strategies, the human visual system proves that simple categories can be recognised almost instantaneously. Here we present a system for categorisation from very short sequences (“snippets”) of 1–10 frames, and systematically evaluate it on several data sets. It turns out that even local shape and optic flow for a single frame are enough to achieve ˜80-90% correct classification, and snippets of 5-7 frames (0.2-0.3 seconds of video) yield results on par with the ones state-of-the-art methods obtain on entire video sequences." @default.
- W2493021729 created "2016-08-23" @default.
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- W2493021729 date "2010-05-25" @default.
- W2493021729 modified "2023-10-03" @default.
- W2493021729 title "Fast Categorisation of Articulated Human Motion" @default.
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- W2493021729 doi "https://doi.org/10.4018/978-1-60566-900-7.ch009" @default.
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