PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

What's going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes
Daniel Kuettel, Michael Breitenstein, Luc Van Gool and Vittorio Ferrari
In: CVPR 2010, 13 June - 18 June 2010, San Francisco.


We present two novel methods to automatically learn spatio-temporal dependencies of moving agents in complex dynamic scenes. They allow to discover temporal rules, such as the right of way between different lanes or typical traffic light sequences. To extract them, sequences of activities need to be learned. While the first method extracts rules based on a learned topic model, the second model called DDP-HMM jointly learns co-occurring activities and their time dependencies. To this end we employ Dependent Dirichlet Processes to learn an arbitrary number of infinite Hidden Markov Models. In contrast to previous work, we build on state-of-the-art topic models that allow to automatically infer all parameters such as the optimal number of HMMs necessary to explain the rules governing a scene. The models are trained offline by Gibbs Sampling using unlabeled training data.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:7998
Deposited By:Daniel Kuettel
Deposited On:17 March 2011