Shape Prototype Signatures for Action Recognition

Michael Donoser, Hayko Riemenschneider, Horst Bischof

Abstract: Recognizing human actions in video sequences is frequently based on analyzing the shape of the human silhouette as the main feature. In this paper we introduce a method for recognizing different actions by comparing signatures of similarities to pre-defined shape prototypes. In training, we build a vocabulary of shape prototypes by clustering a training set of human silhouettes and calculate prototype similarity signatures for all training videos. During testing a prototype signature is calculated for the test video and is aligned to each training signature by dynamic time warping. A simple voting scheme over the similarities to the training videos provides action classification results and temporal alignments to the training videos. Experimental evaluation on a reference data set demonstrates that state-of-the-art results are achieved.