Shape Prototype Signatures for Action Recognition

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.