17,330 research outputs found
CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting
Opioid overdose is a growing public health crisis in the United States. This
crisis, recognized as "opioid epidemic," has widespread societal consequences
including the degradation of health, and the increase in crime rates and family
problems. To improve the overdose surveillance and to identify the areas in
need of prevention effort, in this work, we focus on forecasting opioid
overdose using real-time crime dynamics. Previous work identified various types
of links between opioid use and criminal activities, such as financial motives
and common causes. Motivated by these observations, we propose a novel
spatio-temporal predictive model for opioid overdose forecasting by leveraging
the spatio-temporal patterns of crime incidents. Our proposed model
incorporates multi-head attentional networks to learn different representation
subspaces of features. Such deep learning architecture, called
"community-attentive" networks, allows the prediction of a given location to be
optimized by a mixture of groups (i.e., communities) of regions. In addition,
our proposed model allows for interpreting what features, from what
communities, have more contributions to predicting local incidents as well as
how these communities are captured through forecasting. Our results on two
real-world overdose datasets indicate that our model achieves superior
forecasting performance and provides meaningful interpretations in terms of
spatio-temporal relationships between the dynamics of crime and that of opioid
overdose.Comment: Accepted as conference paper at ECML-PKDD 201
Social Attention: Modeling Attention in Human Crowds
Robots that navigate through human crowds need to be able to plan safe,
efficient, and human predictable trajectories. This is a particularly
challenging problem as it requires the robot to predict future human
trajectories within a crowd where everyone implicitly cooperates with each
other to avoid collisions. Previous approaches to human trajectory prediction
have modeled the interactions between humans as a function of proximity.
However, that is not necessarily true as some people in our immediate vicinity
moving in the same direction might not be as important as other people that are
further away, but that might collide with us in the future. In this work, we
propose Social Attention, a novel trajectory prediction model that captures the
relative importance of each person when navigating in the crowd, irrespective
of their proximity. We demonstrate the performance of our method against a
state-of-the-art approach on two publicly available crowd datasets and analyze
the trained attention model to gain a better understanding of which surrounding
agents humans attend to, when navigating in a crowd
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
We present an algorithm for finding temporally consistent occlusion
boundaries in videos to support segmentation of dynamic scenes. We learn
occlusion boundaries in a pairwise Markov random field (MRF) framework. We
first estimate the probability of an spatio-temporal edge being an occlusion
boundary by using appearance, flow, and geometric features. Next, we enforce
occlusion boundary continuity in a MRF model by learning pairwise occlusion
probabilities using a random forest. Then, we temporally smooth boundaries to
remove temporal inconsistencies in occlusion boundary estimation. Our proposed
framework provides an efficient approach for finding temporally consistent
occlusion boundaries in video by utilizing causality, redundancy in videos, and
semantic layout of the scene. We have developed a dataset with fully annotated
ground-truth occlusion boundaries of over 30 videos ($5000 frames). This
dataset is used to evaluate temporal occlusion boundaries and provides a much
needed baseline for future studies. We perform experiments to demonstrate the
role of scene layout, and temporal information for occlusion reasoning in
dynamic scenes.Comment: Applications of Computer Vision (WACV), 2015 IEEE Winter Conference
o
Effects on orientation perception of manipulating the spatio–temporal prior probability of stimuli
Spatial and temporal regularities commonly exist in natural visual scenes. The knowledge of the probability structure of these regularities is likely to be informative for an efficient visual system. Here we explored how manipulating the spatio–temporal prior probability of stimuli affects human orientation perception. Stimulus sequences comprised four collinear bars (predictors) which appeared successively towards the foveal region, followed by a target bar with the same or different orientation. Subjects' orientation perception of the foveal target was biased towards the orientation of the predictors when presented in a highly ordered and predictable sequence. The discrimination thresholds were significantly elevated in proportion to increasing prior probabilities of the predictors. Breaking this sequence, by randomising presentation order or presentation duration, decreased the thresholds. These psychophysical observations are consistent with a Bayesian model, suggesting that a predictable spatio–temporal stimulus structure and an increased probability of collinear trials are associated with the increasing prior expectation of collinear events. Our results suggest that statistical spatio–temporal stimulus regularities are effectively integrated by human visual cortex over a range of spatial and temporal positions, thereby systematically affecting perception
- …