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Gesture recognition
During the blue-c, a marker-less method was developed that detects a
stretched arm as an indication of a pointing gesture from multiple
camera streams. This method allowed basic interaction with the virtual
environment. The aim for blue-c-II is to go towards a more general
gesture recognition framework. Based on the full body pose of the user,
the extended framework will provide more complex interaction schemes
for the collaboration facilities developed during the second project
phase.
The reconstruction of the full body pose from multiple camera
streams without using any markers is still a challenging research task.
Our approach will be based on an articulated body model which will be
fitted to the image data of multiple cameras. The body model is built
out of superellipsoids and is driven by an articulated skeletal
structure. As a first step, by using a background-subtraction
algorithm, the user will be separated from the background in all camera
images. Then, from these foreground masks, the 3D shape of the person
will be computed using a voxel-based procedure. In order to recover the
person’s joint positions and limb dimensions, a stochastic optimization
process will fit the body model into the 3D observation on each time
instant.
Once the movements of the user over time are known, gestures can be
detected and used to trigger actions in the system. For this purpose, a
probabilistic framework will be trained to recognize a set of well
defined gestures, such as grasping, pushing, etc. Using this prior
information, the framework will then classify the recovered temporal
joint data of the performing user into gestures and will trigger the
corresponding actions in the system.
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