A Three Tiered Approach for Articulated Object Action Modeling and Recognition

Visual action recognition is an important problem in computer vision. In this paper, we propose a new method to probabilistically model and recognize actions of articulated objects, such as hand or body gestures, in image sequences. Our method consists of three levels of representation. At the low level, we first extract a feature vector invariant to scale and in-plane rotation by using the Fourier transform of a circular spatial histogram. Then, spectral partitioning [20] is utilized to obtain an initial clustering; this clustering is then refined using a temporal smoothness constraint. Gaussian mixture model (GMM) based clustering and density estimation in the subspace of linear discriminant analysis (LDA) are then applied to thousands of image feature vectors to obtain an intermediate level representation. Finally, at the high level we build a temporal multiresolution histogram model for each action by aggregating the clustering weights of sampled images belonging to that action. W...

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Le Lu, Gregory D. Hager, Laurent Younes
• Keywords: Feature Vectors, Level Representation, NIPS 2004, NIPS 2007, Temporal Multiresolution Histogram