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Sliding window is one direct way to extend a successful recognition system tohandle the more challenging detection problem. While action recognition decidesonly whether or not an action is present in a pre-segmented video sequence,action detection identifies the time interval where the action occurred in anunsegmented video stream. Sliding window approaches for action detection canhowever be slow as they maximize a classifier score over all possiblesub-intervals. Even though new schemes utilize dynamic programming to speed upthe search for the optimal sub-interval, they require offline processing on thewhole video sequence. In this paper, we propose a novel approach for onlineaction detection based on 3D skeleton sequences extracted from depth data. Itidentifies the sub-interval with the maximum classifier score in linear time.Furthermore, it is invariant to temporal scale variations and is suitable forreal-time applications with low latency.

Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets