1,943 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
Recent Advances in Anomaly Detection Methods Applied to Aviation
International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance
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