27,784 research outputs found
Group context learning for event recognition
We address the problem of group-level event recognition from videos. The events of interest are defined based on the motion and interaction of members in a group over time. Example events include group formation, dispersion, fol-lowing, chasing, flanking, and fighting. To recognize these complex group events, we propose a novel approach that learns the group-level scenario context from automatically extracted individual trajectories. We first perform a group structure analysis to produce a weighted graph that repre-sents the probabilistic group membership of the individuals. We then extract features from this graph to capture the mo-tion and action contexts among the groups. The features are represented using the ābag-of-words ā scheme. Finally, our method uses the learned Support Vector Machine (SVM) to classify a video segment into the six event categories. Our implementation builds upon a mature multi-camera multi-target tracking system that recognizes the group-level events involving up to 20 individuals in real-time. 1
Probabilistic prediction of rupture length, slip and seismic ground motions for an ongoing rupture: implications for early warning for large earthquakes
Earthquake EarlyWarning (EEW) predicts future ground shaking based on presently available
data. Long ruptures present the best opportunities for EEW since many heavily shaken areas
are distant from the earthquake epicentre and may receive long warning times. Predicting
the shaking from large earthquakes, however, requires some estimate of the likelihood of the
future evolution of an ongoing rupture. An EEW system that anticipates future rupture using
the present magnitude (or rupture length) together with the Gutenberg-Richter frequencysize
statistics will likely never predict a large earthquake, because of the rare occurrence of
āextreme eventsā. However, it seems reasonable to assume that large slip amplitudes increase
the probability for evolving into a large earthquake. To investigate the relationship between the
slip and the eventual size of an ongoing rupture, we simulate suites of 1-D rupture series from
stochastic models of spatially heterogeneous slip. We find that while large slip amplitudes
increase the probability for the continuation of a rupture and the possible evolution into a
āBig Oneā, the recognition that rupture is occurring on a spatially smooth fault has an even
stronger effect.We conclude that anEEWsystem for large earthquakes needs some mechanism
for the rapid recognition of the causative fault (e.g., from real-time GPS measurements) and
consideration of its āsmoothnessā. An EEW system for large earthquakes on smooth faults,
such as the San Andreas Fault, could be implemented in two ways: the system could issue
a warning, whenever slip on the fault exceeds a few metres, because the probability for a
large earthquake is high and strong shaking is expected to occur in large areas around the
fault. A more sophisticated EEW system could use the present slip on the fault to estimate the
future slip evolution and final rupture dimensions, and (using this information) could provide
probabilistic predictions of seismic ground motions along the evolving rupture. The decision
on whether an EEW system should be realized in the first or in the second way (or in a
combination of both) is user-specific
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
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
This paper proposes a probabilistic approach for the detection and the
tracking of particles in fluorescent time-lapse imaging. In the presence of a
very noised and poor-quality data, particles and trajectories can be
characterized by an a contrario model, that estimates the probability of
observing the structures of interest in random data. This approach, first
introduced in the modeling of human visual perception and then successfully
applied in many image processing tasks, leads to algorithms that neither
require a previous learning stage, nor a tedious parameter tuning and are very
robust to noise. Comparative evaluations against a well-established baseline
show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application
Cognitive visual tracking and camera control
Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
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