A Deep Learning RCNN Approach for Vehicle Recognition in Traffic Surveillance System

Automatic moving vehicle detection and recognition are the crucial steps in traffic surveillance applications. Frame extraction is the prior step, which is followed by box filter based background estimation and removal. Box filter based background estimation is used to smoothen the rapid variations, due to the movement of vehicles. Moving vehicles are then detected by analyzing the pixel wise variations between estimated background and input frames. Vehicle detection phase is then followed by recognition phase to classify variant vehicle classes. The deep learning framework Region based Convolutional Neural Network(RCNN) is implemented for the recognition of vehicles with region proposals. Due to the existence of region proposal in RCNN, computational multiplicity is reduced. Metrices like accuracy, sensitivity, specificity and precision values are evaluated to characterize the proficiency of the proposed methodology for vehicle.