Title
Reasoning about threats: From observables to situation assessment
Author
Burghouts, G.J.
Marck, J.W.
Publication year
2011
Abstract
We propose a mechanism to assess threats that are based on observables. Observables are properties of persons, i.e., their behavior and interaction with other persons and objects. We consider observables that can be extracted from sensor signals and intelligence. In this paper, we discuss situation assessment that is based on observables for threat assessment. In the experiments, the assessment is evaluated for scenarios that are relevant to antiterrorism and crowd control. The experiments are performed within an evaluation framework, where the setup is such that conclusions can be drawn concerning: 1) the accuracy and robustness of an architecture to assess situations with respect to threats; and 2) the architectures dependence of the underlying observables in terms of their false positive and negative rates. One of the interesting conclusions is that discriminative assessment of threatening situations can be achieved by combining generic observables. Situations can be assessed with a precision of 90 at a false positive and negative rate of 15 using only eight learning examples. In a real-world experiment at a large train station, we have classified various types of crowd dynamics. Using simple video features of shape and motion, we have proposed a scheme to translate such features into observables that can be classified by a conditional random field (CRF). The implemented CRF shows to classify successfully the crowd dynamics up to 80 % accuracy. © 2010 IEEE.
Subject
Physics & Electronics
II - Intelligent Imaging
TS - Technical Sciences
Image processing
Architecture
evaluation framework
information processing
observables
situation understanding
threat recognition
Conditional random field
Crowd control
Crowd dynamics
Evaluation framework
False positive
Negative rates
observables
Real world experiment
Sensor signals
Situation assessment
Situation understanding
Threat assessment
threat recognition
Train stations
Video features
Architecture
Data processing
Experiments
Pattern recognition
Terrorism
Rating
To reference this document use:
http://resolver.tudelft.nl/uuid:4b8755a7-ce14-43f8-bd8b-198644479d6a
DOI
https://doi.org/10.1109/tsmcc.2011.2135344
TNO identifier
436018
ISSN
1094-6977
Source
IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 41 (5), 608-616
Document type
article