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Presented by: Nirmalya Ghosh
Abstract:
Vehicle recognition has applications in automated traffic
congestion control, non-stop highroad-toll-centers and sophisticated
security systems. Conventional 2D vehicle-recognition with
input from a single-sensor lacks the robustness and applicability
of the approach to full diurnal cycle. Since it cannot adapt
to environmental changes. In this work, a 3D vehicle recognition
approach is proposed, which integrates the supervisory statistical
learning and dynamic sensor fusion. Cooperation of infrared
and color-video information is proposed to be tuned with the
changing environmental conditions to make the approach applicable
throughout the diurnal cycle. Incremental model building will
be done from the spatio-temporal information of the color-video
and IR frame-sequences for geometry / statistics-based 3D
model of the vehicle in visible and infrared spectral regions.
Physics based visible and infrared models will be developed
considering the vehicle shape and thermodynamics of the vehicle-surfaces.
Environmental changes will be accounted by the learning methods
to tune the cooperative co-evolutionary sensor-fusion strategy
to perform well throughout the day. The Bayesian-type classifier
will consider optimality of feature selection and map these
features to index the 3D database model developed for vehicles
of different makes and shapes.
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