  Different from traditional action recognition based on video segments, online
action recognition aims to recognize actions from unsegmented streams of data
in a continuous manner. One way for online recognition is based on the evidence
accumulation over time to make predictions from stream videos. This paper
presents a fast yet effective method to recognize actions from stream of noisy
skeleton data, and a novel weighted covariance descriptor is adopted to
accumulate evidence. In particular, a fast incremental updating method for the
weighted covariance descriptor is developed for accumulation of temporal
information and online prediction. The weighted covariance descriptor takes the
following principles into consideration: past frames have less contribution for
recognition and recent and informative frames such as key frames contribute
more to the recognition. The online recognition is achieved using a simple
nearest neighbor search against a set of offline trained action models.
Experimental results on MSC-12 Kinect Gesture dataset and our newly constructed
online action recognition dataset have demonstrated the efficacy of the
proposed method.
