Using Bilinear Models for View-invariant Action and Identity Recognition

Human identification from gait is a challenging task in realistic surveillance scenarios in which people walking along arbitrary directions are shot by a single camera. In this paper, motivated by the view-invariance issue in the human ID from gait problem, we address the general problem of classifying the "content" of human motions of unknown "style". Given a dataset of sequences in which different people walking normally are seen from several well-separated views, we propose a three-layer scheme based on bilinear models, in which image sequences are mapped to observation vectors of fixed dimension using Markov modeling. We test our approach on the CMU Mobo database, showing how bilinear separation outperforms other approaches, opening the way to view- and action-invariant identity recognition, as well as subject- and view-invariant action recognition.

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Fabio Cuzzolin
• Keywords: Bilinear Models, Bilinear Separation Outperforms, Cmu Mobo Database, Computer Vision, CVPR 2006, Gait Problem, Realistic Surveillance Scenarios