PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Wrong turn - no dead end: a stochastic pedestrian motion model
S Pellegrini, A Ess, M Tanaskovic and Luc Van Gool
In: International workshop on socially intelligent surveillance and monitoring - SISM 2010, 14 June 2010, San Francisco, USA.

Abstract

This paper addresses the use of social behavior models for the prediction of a pedestrian’s future motion. Recently, such models have been shown to outperform simple constant velocity models in cases where data association becomes ambiguous, e.g. in case of occlusion, bad image quality, or low frame rates. However, to account for the multiple alternatives a pedestrian can choose from, one has to go beyond the currently available deterministic models. To this end, we propose a stochastic extension of a recently proposed simulation-based motion model. This new instantiation can cater for the possible behaviors in an entire scene in a multi-hypothesis approach, using a principled modeling of uncertainties. In a set of experiments for prediction and template-based tracking, we compare it to a deterministic instantiation and investigate the general value of using an advanced motion prior in tracking.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:7983
Deposited By:Luc Van Gool
Deposited On:17 March 2011