Human action recognition using Local Spatio-Temporal Discriminant Embedding

Human action video sequences can be considered as nonlinear dynamic shape manifolds in the space of image frames. In this paper, we address learning and classifying human actions on embedded low-dimensional manifolds. We propose a novel manifold embedding method, called Local Spatio-Temporal Discriminant Embedding (LSTDE). The discriminating capabilities of the proposed method are two-fold: (1) for local spatial discrimination, LSTDE projects data points (silhouette-based image frames of human action sequences) in a local neighborhood into the embedding space where data points of the same action class are close while those of different classes are far apart; (2) in such a local neighborhood, each data point has an associated short video segment, which forms a local temporal subspace on the embedded manifold. LSTDE finds an optimal embedding which maximizes the principal angles between those temporal subspaces associated with data points of different classes. Benefiting from the joint ...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Kui Jia, Dit-Yan Yeung
• Keywords: Computer Vision, CVPR 2008, Human Action Sequences, Human Action Video, Manifold Embedding Methods, Optimal Embedding, Spatio-temporal Discriminant Embedding