Research Overview | ||
Our approaches to model-based recognition
first considered human motion to be that of a moving
pendulum. This was also used to find subjects with
articulated motion. It further allowed deployment of a
gait signature based on the spectrum of the variation in
inclination of the human thigh. Essentially, gait was
modelled as Simple Harmonic Motion (SHM) and the
cue to identity was the difference between perceived
motion and that of pure SHM. As our particular focus was
the variation in inclination of the thigh, we have also
studied the nature of its variation, and developed
measures that were invariant to trajectory and
derived from the data itself. Recently, we have moved to
using a continuous model
formulation, based on our other work in feature
extraction, leading to the notion that recognition can be
based on motion templates. More recently, and especially
for generality, we have deployed our evidence gathering
techniques for 3D based recognition which also
allows possibility of gait-motion capture via a marker-less
system. |
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Our statistical approaches started from the notion that these approaches have proven application potential in automatic gait recognition. We first deployed Principal Components Analysis for data compression, coupled with Canonical Analysis for improved (and practicable) recognition, processing thresholded-image and optical flow data. Though this appears to be very promising, it could be reinforced by relationship to the mechanics of gait. This has been achieved by a new approach that uses velocity moments (statistical moments computed for moving objects). |