Car recognition from frontal images in mobile environment

Recognition of car make and model from frontal images is a common problem in computer vision. We refined existing approaches based on ROIs defined relative to the number plate. Square-Mapped-Gradient features are extracted from the ROI and recognition is accomplished by classification utilizing a learning set. The classifier is evaluated using ground truth data provided manually. Via numerical simulations we evaluated the detection tolerance of the method and proposed semi-automatic and fully automatic methods. The SMG-based classification is able to give nearly perfect results when there is no outlier class, which decreases to 92% and 87% in case of the semi-automatic and fully automatic methods, respectively. Separation between outliers and known types can be balanced by a threshold. Since the size of the learning set can be kept low and the size of the SMG features are small, this approach can be successfully used to solve mobile client-server scenarios.