Synthesis of an adaptive CPR filter for identification of vehicle make & type

A methodology for recognition of vehicle make and type by identifying logo images is proposed in this paper. Angular displacements in the target images, add complexity to the identification process in real time scenario. Slight deviations in the input scene render the output invariably meaningless. The correlation pattern recognition based CPR filters, if amicably trained for the images containing requisite trend of angular distortions, can produce remarkable identification results invariant to the speculated angular rotations. Maximum Average Correlation Height MACH Filter being a major development in CPR field, duly trained with images of various classes in parallel, carries adequate statistical information and intelligence to ensure correct classification results. A supervised learning process of the proposed filter enables us to have an adaptive design to cater for misclassifications tagged as false positives in the first instance of training. Synthesis of a flexible, constantly evolving and an adaptive classifier can conveniently manage correct detection of target images irrespective of acute angular shifts in the input scenes.