A Two Formal Languages based Model for Representing Human Activities

摘要

Within this paper, we present a novel method for view-independent simple human activity recognition from video frames. In order to tackle the problem, we are going to reduce the number of frames produce by a video sequence, since we are positive that we can identify activities from sparsely sampled sequence of body poses. Then we will use a cooperative set of formal languages. Named SOMA and KINISIS language respectively. SOMA language represents various information regarding the human body (state) in a frame and will assign to it a unique timestamp. While KINISIS language is a sequence based formal language that is going to use the information extracted from SOMA language and segment simple activities into separate actions and then correctly identify each one of them.