Learning Higher-Order Markov Models for ObjectTracking in Image Sequences
2009 (English)In: Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II, Berlin, Heidelberg: Springer-Verlag , 2009, 184-195 p.Conference paper (Refereed)
This work presents a novel object tracking approach, where the motion model is learned from sets of frame-wise detections with unknown associations. We employ a higher-order Markov model on position space instead of a first-order Markov model on a high-dimensional state-space of object dynamics. Compared to the latter, our approach allows the use of marginal rather than joint distributions, which results in a significant reduction of computation complexity. Densities are represented using a grid-based approach, where the rectangular windows are replaced with estimated smooth Parzen windows sampled at the grid points. This method performs as accurately as particle filter methods with the additional advantage that the prediction and update steps can be learned from empirical data. Our method is compared against standard techniques on image sequences obtained from an RC car following scenario. We show that our approach performs best in most of the sequences. Other potential applications are surveillance from cheap or uncalibrated cameras and image sequence analysis.
Place, publisher, year, edition, pages
Berlin, Heidelberg: Springer-Verlag , 2009. 184-195 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 5876
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-50495DOI: 10.1007/978-3-642-10520-3_17ISI: 000279247100017ISBN: 978-3-642-10519-7OAI: oai:DiVA.org:liu-50495DiVA: diva2:342945
The 5th International Symposium on Advances in Visual Computing (ISVC), Las Vegas, USA, December