Sebastian Nowozin and Jamie Shotton
9 July 2012
Applications of human action recognition in interactive systems such as games
require the robust real-time recognition of human actions at low latencies
from a stream of observations.
The current paradigms of action recognition either treat the pre-segmented
sequence as a whole unit to be classified, or classify a range of frames as
action, evaluating the performance using a frame-by-frame measure.
We argue that both paradigms are limited when addressing latency requirements.
Instead, we propose the notion of “action points” to serve as natural
temporal anchors of simple human actions. Action points enable latency-aware
training and evaluation of online recognition systems.
To demonstrate the usefulness of action points we show how two different
systems, a Hidden Markov Model and a direct classification approach can be
used with action point annotations.
We evaluate our approach on two data sets with different input modalities
and show that our abstraction of action points is useful in settings where
human action recognition has to be performed online and at low latencies.
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