14,252 research outputs found
leave a trace - A People Tracking System Meets Anomaly Detection
Video surveillance always had a negative connotation, among others because of
the loss of privacy and because it may not automatically increase public
safety. If it was able to detect atypical (i.e. dangerous) situations in real
time, autonomously and anonymously, this could change. A prerequisite for this
is a reliable automatic detection of possibly dangerous situations from video
data. This is done classically by object extraction and tracking. From the
derived trajectories, we then want to determine dangerous situations by
detecting atypical trajectories. However, due to ethical considerations it is
better to develop such a system on data without people being threatened or even
harmed, plus with having them know that there is such a tracking system
installed. Another important point is that these situations do not occur very
often in real, public CCTV areas and may be captured properly even less. In the
artistic project leave a trace the tracked objects, people in an atrium of a
institutional building, become actor and thus part of the installation.
Visualisation in real-time allows interaction by these actors, which in turn
creates many atypical interaction situations on which we can develop our
situation detection. The data set has evolved over three years and hence, is
huge. In this article we describe the tracking system and several approaches
for the detection of atypical trajectories
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
Stealthy Deception Attacks Against SCADA Systems
SCADA protocols for Industrial Control Systems (ICS) are vulnerable to
network attacks such as session hijacking. Hence, research focuses on network
anomaly detection based on meta--data (message sizes, timing, command
sequence), or on the state values of the physical process. In this work we
present a class of semantic network-based attacks against SCADA systems that
are undetectable by the above mentioned anomaly detection. After hijacking the
communication channels between the Human Machine Interface (HMI) and
Programmable Logic Controllers (PLCs), our attacks cause the HMI to present a
fake view of the industrial process, deceiving the human operator into taking
manual actions. Our most advanced attack also manipulates the messages
generated by the operator's actions, reversing their semantic meaning while
causing the HMI to present a view that is consistent with the attempted human
actions. The attacks are totaly stealthy because the message sizes and timing,
the command sequences, and the data values of the ICS's state all remain
legitimate.
We implemented and tested several attack scenarios in the test lab of our
local electric company, against a real HMI and real PLCs, separated by a
commercial-grade firewall. We developed a real-time security assessment tool,
that can simultaneously manipulate the communication to multiple PLCs and cause
the HMI to display a coherent system--wide fake view. Our tool is configured
with message-manipulating rules written in an ICS Attack Markup Language (IAML)
we designed, which may be of independent interest. Our semantic attacks all
successfully fooled the operator and brought the system to states of blackout
and possible equipment damage
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