A Combined Probabilistic Framework for Learning Gestures and Actions

Abstract. In this paper we introduce a probabilistic approach to support visual supervision and gesture recognition. Task knowledge is both of geometric and visual nature and it is encoded in parametric eigenspaces. Learning processes for compute modal subspaces (eigenspaces) are the core of tracking and recognition of gestures and tasks. We describe the overall architecture of the system and detail learning processes and gesture design. Finally we show experimental results of tracking and recognition in block-world like assembling tasks and in general human gestures. Keywords. Visual Inspection, Gesture Recognition, Learning, Probabilistic Constraints, Eigenmethods

• Type: Conference
• Year: 1998
• Where: IEAAIE
• Authors: Francisco Escolano, Miguel Cazorla, Domingo Gallardo, Faraón Llorens, Rosana Satorre, Ramón Rizo
• Keywords: Artificial Intelligence, Compute Modal Subspaces, Gesture, Gesture Recognition, IEAAIE 1998