wilamowski-b-m-irwin-j-d-industrial-communication-systems-2011

Processing Data in Complex Communication Systems 68-7
Log-likelihood Sensor values
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68.6 Intelligent Surveillance Systems
Today, surveillance has become a relevant means for protecting public and industrial areas against
malicious subjects like burglars or vandals. For both keeping privacy of irreproachable citizens as well
as enabling automated detection of potential threats, computer-based systems are needed that support
human operators in recognizing unusual situations. A suitable approach for that purpose is to utilize
a hierarchical architecture of semantic processing layers distributed in a network of nodes. The goal of
these layers is to learn the “normality” in the environment of the network, in order to detect unusual
situations and to inform the human operator in such cases. The SENSE project [WSe06,BKV+08] implements
such an architecture. Therefore, we will briefly cover it here as an example of how hierarchical
semantic processing can be used for surveillance systems.
SENSE consists of a network of communicating sensor nodes, each equipped with a camera and a
microphone array. These sensor modalities observe their environment and deliver streams of mono-modal
events to a reasoning unit, which derives fused high-level observations from this information. These
observations are exchanged with neighbor nodes in order to establish a global view about the commonly
observed environment. Detected potential threats are finally reported to the person(s) in charge.
Though most of the methods used in the particular layers are widely used in many applications, the
benefit lies in the combination of them in order to let the messages of the system really appear meaningful
to the user.
68.6.1 architecture
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FIGURE 68.2 Sensor value and log-likelihood for a single sensor from the system. Unusual sensor values register
as drops in the log-likelihood, causing alarms.
In this case, an eight-layer data processing architecture is adopted, in which the lower layers are responsible
for a stable and comprehensive world representation to be evaluated in the higher layers (Figure 68.3).
First, the visual low-level feature extraction (layer 0) processes frame by frame from the camera in
2D camera coordinates and extracts predefined visual objects. At the same time, the audio low-level
extraction scans the acoustic signals from a linear eight-microphone array for trained sound patterns of
predefined categories. Due to limited processing capabilities, this layer can deliver significantly unstable
data in both modalities. In case of unfortunate conditions for the camera, detected symbols can change
their label from one category to another and back for the same physical object within consecutive frames.
The size of detected symbols can change from small elements to large ones covering tens of square
© 2011 by Taylor and Francis Group, LLC