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Egocentric Visual Event Classification with Location-Based Priors

Sudeep Sundaram, Walterio Mayol-Cuevas, Egocentric Visual Event Classification with Location-Based Priors. International Symposium on Visual Computing. November 2010. PDF, 1494 Kbytes.


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.

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