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Vivek Kwatra
College of Computing
Georgia Institute of Technology
Temporal Integration of Multiple Silhouette-based Body-part Hypotheses
Online Slide Handouts
A method for temporally integrating appearance-based body-part labelling is
presented. We begin by modifying the silhouette labelling method of "Ghost"
(see below); that system first determines which posture best describes the
person currently and then uses posture-specific heuristics to generate
labels for head, hands, and feet. Our approach is to assign a posture
probability and then estimate body part locations for all possible postures.
Next we temporally integrate these estimates by finding a best path through
the posture-time lattice. A density-sampling propagation approach is used
that allows us to model the multiple hypotheses resulting from consideration
of different postures. We show quantitative and qualitative results where
the temporal integration solution improves the instantaneous estimates.
This method can be applied to any system that inherently has multiple
methods of asserting instantaneous properties but from which a temporally
coherent interpretation is desired.
"Ghost: A Human Body Part Labeling System Using Silhouettes"
I. Haritaoglu, D. Harwood and L. Davis
Proc. ICPR 1998, pp 77-82
Amos Y. Johnson Jr.
College of Computing
Georgia Institute of Technology
Gait Recognition Using Static, Activity-Specific Parameters
Online Slide Handouts
Abstract: A gait-recognition technique that recovers static body and stride
parameters of subjects as they walk is presented. This approach is an
example of an activity-specific biometric: a method of extracting
identifying properties of an individual or of an individual's behavior that
is applicable only when a person is performing that specific action. To
evaluate our parameters, we derive an expected confusion metric --- related
to mutual information --- as opposed to reporting a percent correct with a
limited database. This metric predicts how well a given feature vector will
filter identity in a large population. We test the utility of a variety of
body and stride parameters recovered in different viewing conditions on a
database consisting of 15 to 20 subjects walking at both an angled and
frontal-parallel view with respect to the camera, both indoors and out. We
also analyze motion-capture data of the subjects to discover whether
confusion in the parameters is inherently a physical or a visual measurement
error property.
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