Bayesian prior models for vehicle make and model recognition

Abstract

Automatic vehicle type recognition (make and model) is very useful in secure access and traffic monitoring applications. It compliments the number plate recognition systems by providing a higher level of security against fraudulent use of number plates in traffic crimes. In this paper we present a simple but powerful probabilistic framework for vehicle type recognition that requires just a single representative car image in the database to recognize any incoming test image exhibiting strong appearance variations, as expected in outdoor image capture e.g. illumination, scale etc. We propose to use a new feature description, local energy based shape histogram 'LESH', in this problem that encodes the underlying shape and is invariant to illumination and other appearance variations such as scale, perspective distortions and color. Our method achieves high accuracy (above 94%) as compared to the state of the art previous approaches on a standard benchmark car dataset. It provides a posterior over possible vehicle type matches which is especially attractive and very useful in practical traffic monitoring and/or surveillance video search (for a specific vehicle type) applications.