BMVC 2004, Kingston, 7th-9th Sept, 2004
Analysis of Features for Rigid Structure Vehicle Type Recognition
V.S. Petrovic and T. Cootes (University of Manchester)
We describe an investigation into feature representations for rigid structure
recognition framework for recognition of objects with a multitude of
classes. The intended application is automatic recognition of vehicle type
for secure access and traffic monitoring applications, a problem not hitherto
considered at such a level of accuracy. We demonstrate that a relatively simple
set of features extracted from sections of car front images can be used
to obtain high performance verification and recognition of vehicle type (both
car model and class). We describe the approach and resulting system in full,
and the results of experiments comparing a wide variety of different features.
The final system is capable of recognition rates of over 93% and verification
equal error rates of fewer than 5.6% when tested on over 1000 images
containing 77 different classes. The system is shown to be robust for a wide
range of weather and lighting conditions.