Section: New Results

Skeletal Quads

Recent advances on human motion analysis have made the extraction of human skeleton structure feasible, even from single depth images. This structure has been proven quite informative for discriminating actions in a recognition scenario. In this context, we propose a local skeleton descriptor that encodes the relative position of joint quadruples. Such a coding implies a similarity normalization transform that leads to a compact (6D or 5D) view-invariant skeletal feature, referred to as skeletal quad. In the references below, we use this descriptor in conjunction with Fisher kernel in order to encode gesture or action (sub)sequences. The short length of the descriptor compensates for the large inherent dimensionality associated to Fisher vectors. We investigate the performance in both isolated [28] and continuous [27] recognition scenarios.

Website: https://team.inria.fr/perception/research/skeletalquads/