Machine Learning Algorithms for Surveillance and Event Detection

In conjunction with the International Conference on Machine Learning 2006
Carnegie Mellon University, Pittsburgh, PA
June 29, 2006


Denver Dash, Intel Research (
Dragos Margineantu, Boeing, Mathematics and Computing Technology (
Terran Lane, University of New Mexico, (
Weng-Keen Wong, Oregon State University, (

Workshop Description

A common task in many domains involves monitoring routinely collected data for anomalous events. This task is prevalent in surveillance and also in analysis of scientific data. We will refer to this monitoring process as event detection. Event detection has the potential to impact a wide range of important real-world applications, ranging from security, finance, public health, medicine, biology, environmental science, manufacturing, astrophysics, business and economics. In the recent past, human beings have had the laborious job of manually examining the collected data for events of interest. With the emergence of computers, many efforts have been made to replace manual inspection with an automated process. Data, however, have become increasingly complex in recent years. Multivariate records, images, video footage, audio recordings, spatial and spatio-temporal data, text documents, and even relational data are now routinely collected. One might expect that existing work in machine learning would be well-suited for this task. However, in practice, the peculiarities of the application often grossly violate the standard assumptions of machine learning. As a result, new algorithms need to be created in order to address these issues and fill an important gap in machine learning research which would impact many of the most pressing real-world applications being studied today. The focus of this workshop will be on machine learning algorithms for surveillance and event detection in complex forms of data, novel application areas for event detection, and new directions for this type of research.

Important Dates

Workshop Proceedings

Framework for Anomalous Change Detection
James Theiler, Simon Perkins

Shape Outlier Detection Using Pose Preserving Dynamic Shape Models
Chan-Su Lee, Ahmed Elgammal

Learning Sequential Models for Detecting Anomalous Protocol Usage
Lloyd Greenwald

A Wavelet-based Anomaly Detector for Early Detection of Disease Outbreaks
Thomas Lotze, Galit Shmueli, Sean Murphy, Howard Burkom

Towards a Learning Traffic Incident Detection System
Tomas Singliar, Milos Hauskrecht

Bayesian Anomaly Detection
Tim Menzies, David Allen


The Boeing Company and Intel will provide financial support for the workshop in order to facilitate travel for participants with accepted papers.