54,290 research outputs found
Lost in Time: Temporal Analytics for Long-Term Video Surveillance
Video surveillance is a well researched area of study with substantial work
done in the aspects of object detection, tracking and behavior analysis. With
the abundance of video data captured over a long period of time, we can
understand patterns in human behavior and scene dynamics through data-driven
temporal analytics. In this work, we propose two schemes to perform descriptive
and predictive analytics on long-term video surveillance data. We generate
heatmap and footmap visualizations to describe spatially pooled trajectory
patterns with respect to time and location. We also present two approaches for
anomaly prediction at the day-level granularity: a trajectory-based statistical
approach, and a time-series based approach. Experimentation with one year data
from a single camera demonstrates the ability to uncover interesting insights
about the scene and to predict anomalies reasonably well.Comment: To Appear in Springer LNE
Modelling potential movement in constrained travel environments using rough space-time prisms
The widespread adoption of location-aware technologies (LATs) has afforded analysts new opportunities for efficiently collecting trajectory data of moving individuals. These technologies enable measuring trajectories as a finite sample set of time-stamped locations. The uncertainty related to both finite sampling and measurement errors makes it often difficult to reconstruct and represent a trajectory followed by an individual in space-time. Time geography offers an interesting framework to deal with the potential path of an individual in between two sample locations. Although this potential path may be easily delineated for travels along networks, this will be less straightforward for more nonnetwork-constrained environments. Current models, however, have mostly concentrated on network environments on the one hand and do not account for the spatiotemporal uncertainties of input data on the other hand. This article simultaneously addresses both issues by developing a novel methodology to capture potential movement between uncertain space-time points in obstacle-constrained travel environments
HD 69686: A Mysterious High Velocity B Star
We report on the discovery of a high velocity B star, HD 69686. We estimate
its space velocity, distance, surface temperature, gravity, and age. With these
data, we are able to reconstruct the trajectory of the star and to trace it
back to its birthplace. We use evolutionary tracks for single stars to estimate
that HD 69686 was born 73 Myr ago in the outer part of our Galaxy (
kpc) at a position well below the Galactic plane ( kpc), a very
unusual birthplace for a B star. Along the star's projected path in the sky, we
also find about 12 other stars having similar proper motions, and their
photometry data suggest that they are located at the same distance as HD 69686
and probably have the same age. We speculate on the origin of this group by
star formation in a high velocity cloud or as a Galactic merger fragment.Comment: 28 pages, 6 figures, accepted for publication in Ap
Discovering private trajectories using background information
Trajectories are spatio-temporal traces of moving objects which contain valuable information to be harvested by spatio-temporal data mining techniques. Applications like city traffic planning, identification of evacuation routes, trend detection, and many more can benefit from trajectory mining. However, the trajectories of individuals often contain private and sensitive information, so anyone who possess trajectory data must take special care when disclosing this data. Removing identifiers from trajectories before the release is not effective against linkage type attacks, and rich sources of background information make it even worse. An alternative is to apply transformation techniques to map the given set of trajectories into another set where the distances are preserved. This way, the actual trajectories are not released, but the distance information can still be used for data mining techniques such as clustering. In this paper, we show that an unknown private trajectory can be reconstructed using the available background information together with the mutual distances released for data mining purposes. The background knowledge is in the form of known trajectories and extra information such as the speed limit. We provide analytical results which bound the number of the known trajectories needed to reconstruct private trajectories. Experiments performed on real trajectory data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds
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