205 research outputs found
Multi-view object tracking using sequential belief propagation
peer reviewedMultiple cameras and collaboration between them make possible the integration of information available from multiple views and reduce the uncertainty due to occlusions. This paper presents a novel method for integrating and tracking multi-view observations using bidirectional belief propagation. The method is based on a fully connected graphical model where target states at different views are represented as different but correlated random variables, and image observations at a given view are only associated with the target states at the same view. The tracking processes at different views collaborate with each other by exchanging information using a message passing scheme, which largely avoids propagating wrong information. An efficient sequential belief propagation algorithm is adopted to perform the collaboration and to infer the multi-view target states. We demonstrate the effectiveness of our method on video-surveillance sequences.TRICTRA
Effect of Optimizer, Initializer, and Architecture of Hypernetworks on Continual Learning from Demonstration
In continual learning from demonstration (CLfD), a robot learns a sequence of
real-world motion skills continually from human demonstrations. Recently,
hypernetworks have been successful in solving this problem. In this paper, we
perform an exploratory study of the effects of different optimizers,
initializers, and network architectures on the continual learning performance
of hypernetworks for CLfD. Our results show that adaptive learning rate
optimizers work well, but initializers specially designed for hypernetworks
offer no advantages for CLfD. We also show that hypernetworks that are capable
of stable trajectory predictions are robust to different network architectures.
Our open-source code is available at
https://github.com/sebastianbergner/ExploringCLFD
- …