Learning a Pose Lexicon for Semantic Action Recognition
This work addresses action recognition for applications like gesture and exercise analysis, presenting a novel approach but with incremental improvements in a specific domain.
The paper tackles the problem of semantic action recognition by learning a pose lexicon that maps textual instructions to visual poses, enabling action recognition through maximum translation probability. Experiments on MSRC-12 and WorkoutSu-10 datasets verified the method's efficacy in pre-trained and zero-shot settings.
This paper presents a novel method for learning a pose lexicon comprising semantic poses defined by textual instructions and their associated visual poses defined by visual features. The proposed method simultaneously takes two input streams, semantic poses and visual pose candidates, and statistically learns a mapping between them to construct the lexicon. With the learned lexicon, action recognition can be cast as the problem of finding the maximum translation probability of a sequence of semantic poses given a stream of visual pose candidates. Experiments evaluating pre-trained and zero-shot action recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets were used to verify the efficacy of the proposed method.