Distributed solutions for visual
sensor networks to detect targets
in crowds
Cheng Qian
Outline
• Visual sensor networks for target detection
• Computing paradigms in sensor networks
• Local processing
• A centralized solution
• Distributed solution I
• Distributed solution II
Visual sensor networks
The technology involves deploying (manually or from a plane) a large number of small,
inexpensive motes over the area of interest.
Each mote carries
Visual sensors ( CCD or thermal ) with limited
range and field of view (FOV).
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Limited computing capacities and storage resources.
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Wireless channels to communicate with other sensors
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Why sensor networks for an application about target detection ?
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Existence? A target may be occluded from the vision of “a” mote.
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Localization? A visual mote is more like a orientation sensor.
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3D Shape of the target? A mote only captures a 2D silhouette of the target.
Collaboration among motes in the entire network.
Visual sensor networks
Difference from a multi-perspective system where
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each camera has no data processing capacity.
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cameras are deployed with a planned strategy.
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cameras are never enough to be called densely deployed
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no energy and bandwidth concerns
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A powerful central station
Key sentences in visual sensor networks for target detection
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The local processing capacity should be fully exploited.
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Sensors should be dynamically aware of the location of neighboring sensors
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Decision should be made by integrating information captured by the entire network.
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Redundant information should be discarded and data should be only directed to related sensors.
Trade-off between information integration and information transmission.
Visual sensor networks-Computing paradigms
In the context of sensor networks, computing paradigm refers to the information processing model
deployed in the application layer of the protocol stack.
Centralized client/server model
Distributive peer to peer model
Information integration:
entire network
local motes
Wireless transmission:
long distance
short distance
Networking traffic jam:
almost certainly
almost impossible
Related motes are clustered, but how to
define “motes being related”??
Distributive cluster-based model
Illustrations from Xiaolin Wang’s thesis
Local processing in a Mote
Each mote encloses useful information into limited bits.
Raw image
Silhouette
Silhouette boundary
A mote
Remove noises
Remove spikes
Ridges
A target may be projected to a ridge in an image, and each ridge is represented by its central axis, height
and colors.
A centralized solution
Integrating ridges from all the motes for detection and localization - A spot is seen having a
ridge by multiple motes can be declared to be occupied by a target. The declaration gets more
confirmed with the number of contributing motes increases.
1.
Sweep the ridge through the common ground.
and drop votes to spots the ridge passes by
2.
Select the spot with highest number of
compatible votes ( same color, same height…).
This spot is declared to be occupied by a target.
3.
Find all the ridges contributing to this
declaration. Cancel all the other votes created
by these ridges. Remove those spots between
the declared spot and the ridges.
4.
Go back to step 2.
To save memory and increase running time, the
ground plane is implemented by a quad-tree
structure instead of a grid.
A centralized solution
Shape reconstruction - A visual hull.
Transmit all the critical points along the boundary of the ridge,
and stack up the “slices” of visual hull.
A distributed solution I
Cluster related sensors
1. Distance smaller than d --- Ball Pivoting
d
2. FOV overlapped
--- A convex hull.
3. Information is exchanged between neighboring clusters through motes on the cluster boundaries.
A distributed solution II
Recall the centralized solution
What about meshing the motes, and each mesh with three
vertex motes forms a cluster? Each cluster is only responsible
for detecting local occurrences inside that mesh.
1. Decomposed a central task requiring storage of
a global map and computation about global
optimization into local tasks belonging to each
mesh.
2. Neighboring meshes can exchange information
with each other to reduce redundancy.
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