Mr Kiran Varanasi / Dr Fabio Cuzzolin Applicant Career Summary

Mr Kiran Varanasi / Dr Fabio Cuzzolin Newton International Fellowships - 2010
Created: Friday, January 15, 2010 15:09 [Approved]
During my Ph.D, I have worked on the problem of motion estimation without
segmenting an object from background clutter. I demonstrated results on scenes
involving multiple actors interacting with each other. To achieve this, I used a
mesh evolution framework that handles topological changes. I proposed methods
for computing local 3D features on objects, and algorithms for matching shapes
that rely on such local features. Such methods can handle partial shape matching,
and work even when the object is not entirely visible to the sensor.
In the current project, we aim at combining the strengths of the two approaches
developed by applicant and co-applicant and fit them into a statistical learning
framework. The first challenge that we face is the huge dimensionality of the input
data. Even though adaptive spectral embeddings translate this input into a new
space of much lower dimensionality, they have to be first learnt by training. When
the dimensionality of the input data is prohibitively large (as is in our case), the
learning takes too much time and needs too much memory space. We would like
to attack this problem by using the idea of "compressed sensing" that has recently
become popular in the signal processing community. We shall code the input data
using random projections that are linear, sparsity oriented and metric-preserving.
This vastly reduces the dimensionality of the feature space and makes adaptive
embeddings tractable.
Adaptive spectral embeddings can handle global matching, but not partial
matching. Our second challenge is to fit these embeddings into a partial matching
framework. Similar to my Ph.D work, we would like to derive feature descriptors
on these embeddings that are local in scope, and which can be used for matching
shapes amidst background clutter. Ideally, we should be able to learn body-parts
of the shapes, with their distinctive shape signatures.
The third and final challenge that we will have to address is that of unsupervised
learning over a large database of example shapes and poses. This learning will
further improve the descriptiveness of our features. For example, statistical
learning can restrict the lower arm of a human body to have a joint angle not more
than 180 degrees with the upper arm. We seek to learn the shape and motion
statistics of arbitrary shapes, not just human beings. For example, we can learn
the statistics of men riding bicycles, or of children playing with their pets.
The potential applications of the proposed work are numerous. A straightforward
application, for instance, is kinematic motion capture in outdoor environments,
crucial in the entertainment industry, such as in film production and gaming. A
second natural application is automatic visual surveillance, especially in crowded
and cluttered scenes where the suspect is on the run. An additional example
concerns the assistance of elderly people, through monitoring for signs of ageing
and weakness in their limb movements. A fourth application could be in sports
analysis, where the movements of a sports-person are analysed for their
correctness. Due to the general-purpose nature of our approach, it shall be able
for deployment amidst multiple people performing diverse tasks, and in outdoor
environments. This opens doors for several new applications, to a degree that is
not possible with today's technology.
Apart from practical applications, there shall also be theoretical breakthroughs that
would be of interest, for various other fields that fit in the broad scope of pervasive
computing. Bringing together the two fields of manifold learning and compressed
sensing fits the broader research goal of Dr. Cuzzolin, as outlined by his
collaboration with other European centers of research. The task of analysing
motion (and human activities in particular) fits well with the long-term goals of the
Vision Group of Oxford Brookes University, which has produced several worldclass
publications in this field. I would like to benefit from the strong collaborations
that tie the group in Oxford Brookes to other centres of excellence in the world,
particularly to the Visual Geometry group in the University of Oxford and to the
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