Unsupervised Behavior Change Detection in Multidimensional Data Streams for Maritime Traffic Monitoring

08/14/2019
by   Lucas May Petry, et al.
0

The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities. In this work, we describe research gaps and challenges in machine learning for vessel behavior change and event detection, considering several constraints imposed by real-time data streams and the maritime monitoring domain. As a starting point, we investigate how unsupervised and semi-supervised change detection methods may be employed for identifying shifts in vessel behavior, aiming to detect and label unusual events.

READ FULL TEXT
research
04/07/2020

Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning

The global expansion of maritime activities and the development of the A...
research
08/19/2017

Event-Radar: Real-time Local Event Detection System for Geo-Tagged Tweet Streams

The local event detection is to use posting messages with geotags on soc...
research
01/18/2022

WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data

Detecting relevant changes in dynamic time series data in a timely manne...
research
06/27/2021

Score-Based Change Detection for Gradient-Based Learning Machines

The widespread use of machine learning algorithms calls for automatic ch...
research
08/12/2020

Detection of Abnormal Vessel Behaviours from AIS data using GeoTrackNet: from the Laboratory to the Ocean

The constant growth of maritime traffic leads to the need of automatic a...
research
08/10/2022

A Monitoring and Discovery Approach for Declarative Processes Based on Streams

Process discovery is a family of techniques that helps to comprehend pro...

Please sign up or login with your details

Forgot password? Click here to reset