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Publication - Professor Walterio Mayol-Cuevas

    Egocentric visual event classification with location-based priors

    Citation

    Sundaram, S & Mayol-Cuevas, W, 2010, ‘Egocentric visual event classification with location-based priors’. in: International Symposium on Visual Computing. Springer, pp. 596-605

    Abstract

    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 labelled 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.

    Full details in the University publications repository