A PROBABILISTIC FRAMEWORK FOR PATCH BASED VEHICLE TYPE RECOGNITION

The performance of automatic vehicle identification systems based on automatic number plate recognition can be improved radically with the addition of vehicle make and model recognition systems. Current approaches recognize make and model when the query image contains a properly segmented vehicle with no. background clutter. In this paper, we present a new probabilistic patch based framework to determine make and model of vehicle in presence of background clutter and strong appearance variations e.g. illumination, scale etc. We propose a novel patch selection criterion that automatically learns a discriminative patch set corresponding to vehicle regions for each vehicle class during training phase. In contrast to previous attempts, we intend to recognize make and model of vehicle in highly cluttered background images. Therefore, we have introduced a new challenging dataset of cars with cluttered backgrounds and high in class appearance variations obtained under non-ideal conditions. Work on proposed approach is in progress and it has shown highly competitive results on segmented car dataset and promising results on introduced dataset of cars.