In this paper we tackle the problem of providing a mobile robot with the ability to build a map of its environment using data gathered during navigation. The data correspond to the...
In this paper, we tackle the problem of understanding the temporal structure of complex events in highly varying videos obtained from the Internet. Towards this goal, we utilize a...
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
Monitoring real-time concurrent systems is a challenging task. In this paper we formulate (model-based) supervision by means of hidden state history reconstruction, from event (e.g...