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.
Petrovic and Cootes REF proposed a feature representations for rigid structure recognition to identify objects with an abundant number of categories.
Analysis of Features for Rigid Structure Vehicle Type Recognition
In British Machine Vision Conference
Computer Science