Clustered Spatio-temporal Manifolds for Online Action Recognition
Victoria Bloom, Dimitrios Makris, Vasileios Argyriou · 2014
In this paper, a novel method is presented for low-latency online action recognition from skeleton data. The introduction of pose based features has reduced viewpoint and anthropometric variations, so differing execution rates and personal styles are the major sources of classification error. Previous work for online action recognition fails to adequately address both execution rate and personal style. To overcome these limitations a compression and fusion of offline action recognition approaches has transpired. Specifically, clustered action manifolds are proposed for low computational latency and template fragment matching with peak key poses are introduced for low observational latency. The style invariance of spatio-temporal manifolds is combined with the execution rate invariance of Dynamic Time Warping (DTW). Experimental results on two publicly available datasets demonstrate the high accuracy of the proposed method.