28,094 research outputs found
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
We present a method that learns to integrate temporal information, from a
learned dynamics model, with ambiguous visual information, from a learned
vision model, in the context of interacting agents. Our method is based on a
graph-structured variational recurrent neural network (Graph-VRNN), which is
trained end-to-end to infer the current state of the (partially observed)
world, as well as to forecast future states. We show that our method
outperforms various baselines on two sports datasets, one based on real
basketball trajectories, and one generated by a soccer game engine.Comment: ICLR 2019 camera read
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Mobile phones provide a powerful sensing platform that researchers may adopt
to understand proximity interactions among people and the diffusion, through
these interactions, of diseases, behaviors, and opinions. However, it remains a
challenge to track the proximity-based interactions of a whole community and
then model the social diffusion of diseases and behaviors starting from the
observations of a small fraction of the volunteer population. In this paper, we
propose a novel approach that tries to connect together these sparse
observations using a model of how individuals interact with each other and how
social interactions happen in terms of a sequence of proximity interactions. We
apply our approach to track the spreading of flu in the spatial-proximity
network of a 3000-people university campus by mobilizing 300 volunteers from
this population to monitor nearby mobile phones through Bluetooth scanning and
to daily report flu symptoms about and around them. Our aim is to predict the
likelihood for an individual to get flu based on how often her/his daily
routine intersects with those of the volunteers. Thus, we use the daily
routines of the volunteers to build a model of the volunteers as well as of the
non-volunteers. Our results show that we can predict flu infection two weeks
ahead of time with an average precision from 0.24 to 0.35 depending on the
amount of information. This precision is six to nine times higher than with a
random guess model. At the population level, we can predict infectious
population in a two-week window with an r-squared value of 0.95 (a random-guess
model obtains an r-squared value of 0.2). These results point to an innovative
approach for tracking individuals who have interacted with people showing
symptoms, allowing us to warn those in danger of infection and to inform health
researchers about the progression of contact-induced diseases
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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