36 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
k-Parameter Approach for False In-Season Anomaly Suppression in Daily Time Series Anomaly Detection
Detecting anomalies in a daily time series with a weekly pattern is a common
task with a wide range of applications. A typical way of performing the task is
by using decomposition method. However, the method often generates false
positive results where a data point falls within its weekly range but is just
off from its weekday position. We refer to this type of anomalies as "in-season
anomalies", and propose a k-parameter approach to address the issue. The
approach provides configurable extra tolerance for in-season anomalies to
suppress misleading alerts while preserving real positives. It yields favorable
result.Comment: 5 pages, 7 figure
Análisis de los perfiles de consumo eléctrico paraguayo utilizando Machine Learning
Presentación realizada en el marco del Proyecto PINV18-661: Análisis de la eficiencia energética en edificios no residenciales mediante técnicas metaheurísticas y de inteligencia artificial.CONACYT - Consejo Nacional de Ciencias y TecnologíaPROCIENCI
Detection of anomalous patterns in water consumption: an overview of approaches
The water distribution system constantly aims at improving and efficiently distributing water to the city. Thus, understanding the nature of irregularities that may interrupt or exacerbate the service is at the core of their business model. The detection of technical and non-technical losses allows water companies to improve the sustainability and affordability of the service. Anomaly detection in water consumption is at present a challenging task. Manual inspection of data is tedious and requires a large workforce. Fortunately, the sector may benefit from automatized and intelligent workflows to reduce the amount of time required to identify abnormal water consumption. The aim of this research work is to develop a methodology to detect anomalies and irregular patterns of water consumption. We propose the use of algorithms of different nature that approach the problem of anomaly detection from different perspectives that go from searching deviations from typical behavior to identification of anomalous pattern changes in prolonged periods of time. The experiments reveal that different approaches to the problem of anomaly detection provide complementary clues to contextualize household water consumption. In addition, all the information extracted from each approach can be used in conjunction to provide insights for decision-makingThis research work is cofounded by the European Regional Development Fund (FEDER) under the FEDER Catalonia Operative Programme 2014–2020 as part of the R+D Project from RIS3CAT Utilities 4.0 Community with reference code COMRDI16-1-0057.Peer ReviewedPostprint (author's final draft