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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