Objects can exhibit different dynamics at different scales, and this is often exploited by visual tracking algorithms. A local dynamic model is typically used to extract image features that are then used as input to a system for tracking the entire object using a global dynamic model. Approximate local dynamics may be brittle - point trackers drift due to image noise and adaptive background models adapt to foreground objects that become stationary - but constraints from the global model can make them more robust. We propose a probabilistic framework for incorporating global dynamics knowledge into the local feature extraction processes. A global tracking algorithm can be formulated as a generative model and used to predict feature values that are incorporated into an observation process of the feature extractor. We combine such models in a multichain graphical model framework. We show the utility of our framework for improving feature tracking and thus shape and motion estimates in a batch factorization algorithm. We also propose an approximate filtering algorithm appropriate for online applications, and demonstrate its application to background subtraction.
Combining object and feature dynamics in probabilistic tracking
01.01.2005
876969 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Combining object and feature dynamics in probabilistic tracking
British Library Online Contents | 2007
|Probabilistic Learning and Modelling of Object Dynamics for Tracking
British Library Conference Proceedings | 2001
|A Probabilistic Framework for Combining Tracking Algorithms
British Library Conference Proceedings | 2004
|