Motion Capture Behavior Recognition via Neighborhood Preserving Dictionary Learning

Abstract

Behavior recognition from large available motion capture data has received wide attention in the computer animation community and is growing increasingly important in recent years. In this paper, we present an efficient motion capture behavior recognition approach via neighborhood preserving dictionary learning. First, we normalize all the motion sequences in the database to make the motion to be comparable. Then, the neighborhood preserving property is exploited using Iterative Nearest Neighbors algorithm and subsequently added as a constraint condition for discriminative dictionary learning, whereby the raw motion frame can be represented as a compact set of atoms consisting of neighborhood preserving characteristics. Finally, the recognition result can be efficiently obtained by sparse coding based classification scheme. Extensive experiments tested on publicly available motion capture databases have demonstrated the accuracy and effectiveness of the proposed approach.