A Novel Vehicle Classification Model for Urban Traffic Surveillance Using the Deep Neural Network Model

The vehicle detection is the backbone of the urban surveillance systems, which is used to obtain and identify the various statistics of the urban vehicular mobility. Also the urban surveillance systems are used for the vehicle tracking or vehicular object classification. The proposed model has been designed for the purpose of the urban surveillance and vehicular modelling of the traffic. The proposed model has been designed for the vehicle position identification as well as the vehicle type classification using the deep neural network. The proposed model has been tested with a standard dataset image for the result evaluation. The experimental results has been shown the effectiveness of the proposed model, where the proposed model has been found successful in detection and classification of all of the vehicles in the given image.