PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Pre-attentive Driving Behaviour from Holistic Visual Features
Nicolas Pugeault and Richard Bowden
In: ECCV 2010, 5-11 Sept 2010, Heraklion, Crete.

Abstract

The aim of this paper is to learn driving behaviour by associating the actions recorded from a human driver with pre-attentive visual input, implemented using holistic image features (GIST). All images are labelled according to a number of driving–relevant contextual classes (eg, road type, junction) and the driver’s actions (eg, braking, accelerating, steering) are recorded. The association between visual context and the driving data is learnt by Boosting decision stumps, that serve as input dimension selectors. Moreover, we propose a novel formulation of GIST features that lead to an improved performance for action prediction. The areas of the visual scenes that contribute to activation or inhibition of the predictors is shown by drawing activation maps for all learnt actions. We show good performance not only for detecting driving–relevant con- textual labels, but also for predicting the driver’s actions. The classifier’s false positives and the associated activation maps can be used to focus attention and further learning on the uncommon and difficult situations.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:7104
Deposited By:Nicolas Pugeault
Deposited On:07 March 2011