2,585 research outputs found
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
Pedestrian, Crowd, and Evacuation Dynamics
This contribution describes efforts to model the behavior of individual
pedestrians and their interactions in crowds, which generate certain kinds of
self-organized patterns of motion. Moreover, this article focusses on the
dynamics of crowds in panic or evacuation situations, methods to optimize
building designs for egress, and factors potentially causing the breakdown of
orderly motion.Comment: This is a review paper. For related work see http://www.soms.ethz.c
PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception
The ability to perceive and reason about social interactions in the context
of physical environments is core to human social intelligence and human-machine
cooperation. However, no prior dataset or benchmark has systematically
evaluated physically grounded perception of complex social interactions that go
beyond short actions, such as high-fiving, or simple group activities, such as
gathering. In this work, we create a dataset of physically-grounded abstract
social events, PHASE, that resemble a wide range of real-life social
interactions by including social concepts such as helping another agent. PHASE
consists of 2D animations of pairs of agents moving in a continuous space
generated procedurally using a physics engine and a hierarchical planner.
Agents have a limited field of view, and can interact with multiple objects, in
an environment that has multiple landmarks and obstacles. Using PHASE, we
design a social recognition task and a social prediction task. PHASE is
validated with human experiments demonstrating that humans perceive rich
interactions in the social events, and that the simulated agents behave
similarly to humans. As a baseline model, we introduce a Bayesian inverse
planning approach, SIMPLE (SIMulation, Planning and Local Estimation), which
outperforms state-of-the-art feed-forward neural networks. We hope that PHASE
can serve as a difficult new challenge for developing new models that can
recognize complex social interactions.Comment: The first two authors contributed equally; AAAI 2021; 13 pages, 7
figures; Project page: https://www.tshu.io/PHAS
Multiple-Aspect Analysis of Semantic Trajectories
This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification
PGformer: Proxy-Bridged Game Transformer for Multi-Person Extremely Interactive Motion Prediction
Multi-person motion prediction is a challenging task, especially for
real-world scenarios of densely interacted persons. Most previous works have
been devoted to studying the case of weak interactions (e.g., hand-shaking),
which typically forecast each human pose in isolation. In this paper, we focus
on motion prediction for multiple persons with extreme collaborations and
attempt to explore the relationships between the highly interactive persons'
motion trajectories. Specifically, a novel cross-query attention (XQA) module
is proposed to bilaterally learn the cross-dependencies between the two pose
sequences tailored for this situation. Additionally, we introduce and build a
proxy entity to bridge the involved persons, which cooperates with our proposed
XQA module and subtly controls the bidirectional information flows, acting as a
motion intermediary. We then adapt these designs to a Transformer-based
architecture and devise a simple yet effective end-to-end framework called
proxy-bridged game Transformer (PGformer) for multi-person interactive motion
prediction. The effectiveness of our method has been evaluated on the
challenging ExPI dataset, which involves highly interactive actions. We show
that our PGformer consistently outperforms the state-of-the-art methods in both
short- and long-term predictions by a large margin. Besides, our approach can
also be compatible with the weakly interacted CMU-Mocap and MuPoTS-3D datasets
and achieve encouraging results. Our code will become publicly available upon
acceptance
Data modelling for emergency response
Emergency response is one of the most demanding phases in disaster management. The fire brigade, paramedics, police and municipality are the organisations involved in the first response to the incident. They coordinate their work based on welldefined policies and procedures, but they also need the most complete and up-todate information about the incident, which would allow a reliable decision-making.\ud
There is a variety of systems answering the needs of different emergency responders, but they have many drawbacks: the systems are developed for a specific sector; it is difficult to exchange information between systems; the systems offer too much or little information, etc. Several systems have been developed to share information during emergencies but usually they maintain the nformation that is coming from field operations in an unstructured way.\ud
This report presents a data model for organisation of dynamic data (operational and situational data) for emergency response. The model is developed within the RGI-239 project ‘Geographical Data Infrastructure for Disaster Management’ (GDI4DM)
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