832 research outputs found
Evaluating Crowd Density Estimators via Their Uncertainty Bounds
In this work, we use the Belief Function Theory which extends the
probabilistic framework in order to provide uncertainty bounds to different
categories of crowd density estimators. Our method allows us to compare the
multi-scale performance of the estimators, and also to characterize their
reliability for crowd monitoring applications requiring varying degrees of
prudence
Bayesian Multi Scale Neural Network for Crowd Counting
Crowd Counting is a difficult but important problem in computer vision.
Convolutional Neural Networks based on estimating the density map over the
image has been highly successful in this domain. However dense crowd counting
remains an open problem because of severe occlusion and perspective view in
which people can be present at various sizes. In this work, we propose a new
network which uses a ResNet based feature extractor, downsampling block which
uses dilated convolutions and upsampling block using transposed convolutions.
We present a novel aggregation module which makes our network robust to the
perspective view problem. We present the optimization details, loss functions
and the algorithm used in our work. On evaluating on ShanghaiTech, UCF-CC-50
and UCF-QNRF datasets using MSE and MAE as evaluation metrics, our network
outperforms previous state of the art approaches while giving uncertainty
estimates in a principled bayesian manner.Comment: 10 page
Sparse tree search optimality guarantees in POMDPs with continuous observation spaces
Partially observable Markov decision processes (POMDPs) with continuous state
and observation spaces have powerful flexibility for representing real-world
decision and control problems but are notoriously difficult to solve. Recent
online sampling-based algorithms that use observation likelihood weighting have
shown unprecedented effectiveness in domains with continuous observation
spaces. However there has been no formal theoretical justification for this
technique. This work offers such a justification, proving that a simplified
algorithm, partially observable weighted sparse sampling (POWSS), will estimate
Q-values accurately with high probability and can be made to perform
arbitrarily near the optimal solution by increasing computational power
Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science
The purpose of the New York Workshop on Computer, Earth and Space Sciences is
to bring together the New York area's finest Astronomers, Statisticians,
Computer Scientists, Space and Earth Scientists to explore potential synergies
between their respective fields. The 2011 edition (CESS2011) was a great
success, and we would like to thank all of the presenters and participants for
attending. This year was also special as it included authors from the upcoming
book titled "Advances in Machine Learning and Data Mining for Astronomy". Over
two days, the latest advanced techniques used to analyze the vast amounts of
information now available for the understanding of our universe and our planet
were presented. These proceedings attempt to provide a small window into what
the current state of research is in this vast interdisciplinary field and we'd
like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011
in New York City, Goddard Institute for Space Studie
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