Egocentric Visual Event Classification with Location-Based Priors

We present a method for visual classification of actions and events captured from an egocentric point of view. The method tackles the challenge of a moving camera by creating deformable graph models for classification of actions. Action models are learned from low resolution, roughly stabilized difference images acquired using a single monocular camera. In parallel, raw images from the camera are used to estimate the user's location using a visual Simultaneous Localization and Mapping (SLAM) system. Action-location priors, learned using a labeled set of locations, further aid action classification and bring events into context. We present results on a dataset collected within a cluttered environment, consisting of routine manipulations performed on objects without tags. 1

• Type: Journal
• Year: 2010
• Where: ISVC
• Authors: Sudeep Sundaram, Walterio W. Mayol-Cuevas
• Keywords: Applied Computing, Camera, Deformable Graph Models, ISVC 2010, Single Monocular Camera