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

68-8 Industrial Communication Systems
User notification filter
Layer 8
Detected alarms and
unusual behavior
Predefined
alarm
recognition
Unusual
behavior
recognition
Layer 7
Local and neighbors knowledge
Inter-node communication
Local trajectories map
Layer 6
Neighbor node
Trajectories
HLS and feature models
Parameter inference
High-level symbol (HLS) instances
Layer 5
Layer 4
High-level layers
semantic processing
Sensor fusion (audio, video)
Layer 3
Audio and video object
instances at time t
Tracking of LLSS
Layer 2
What does the node belief at time t
Pre-processing of LLSS
Layer 1
Low-level symbols (LLS)
Audio
Video
Layer 0
Low-level
layer
FIGURE 68.3
High level semantic processing software architecture.
meters and including previously detected and other objects. Consequently, the challenge for higher
levels is to filter out significant information from very noisy data.
The modality events of layer 0 (low-level symbols in Figure 68.3) are checked at layer 1 for plausibility—
e.g., the audio symbols with respect to position and intensity, the video symbols with respect to position and
size. In the second layer, symbols that pass the first spatial check are subdued to a further check, regarding
their temporal behavior. The output of this layer is a more stable and comprehensive world representation
including unimodal symbols. Layer 3 is responsible for sensor fusion [BLS06] the unimodal symbols are
fused here to form multi-modal symbols.
Layer 4 is the parameter inference machine in which probabilistic models for symbol parameters and
events are optimized. The results of this layer are models of high-level symbols (HLSs) and features that
© 2011 by Taylor and Francis Group, LLC