Computer Vision, IEEE International Conference on (2007)
Rio de Janeiro, Brazil
Oct. 14, 2007 to Oct. 21, 2007
Ryan Farrell , Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742. email@example.com
David Doermann , Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742. firstname.lastname@example.org
Larry S. Davis , Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742. email@example.com
We present a Bayesian framework for learning higher-order transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks.
D. Doermann, R. Farrell and L. S. Davis, "Learning Higher-order Transition Models in Medium-scale Camera Networks," 2007 11th IEEE International Conference on Computer Vision(ICCV), Rio de Janeiro, 2007, pp. 1-8.