Counting Repetitive Actions in Event Stream

The frame-based method is not suitable for counting repetitive actions in event stream, since the framing process will disrupt the temporal information of events. For accurate count of repetitive actions in events, we propose a framework based on threefold ideas: a) converting event stream into time series, b) searching candidates of repetitive actions based on the ascending and descending trends of event time series, and c) checking the candidates with a fast dynamic time warping based method. For accurate counting repetitive actions, an action enhancement method for event time series and a Mann-Kendall test incorporated dynamic candidate selection algorithm are innovatively proposed. The experimental results on artificially synthesized and normally recorded event datasets demonstrate that our framework can count repetitive actions in event stream with high accuracy. All codes, datasets and examples of visualization can be found at https://github.com/ZYL618/action_count_in_events3.