3 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
Augmenting Deep Learning Performance in an Evidential Multiple Classifier System
International audienceThe main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier's respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision