I.J. Education and Management Engineering, 2016, 1, 18-31 Published Online January 2016 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2016.01.03 Available online at http://www.mecs-press.net/ijeme A Novel Vehicle Classification Model for Urban Traffic Surveillance Using the Deep Neural Network Model Kamini Goyal, Dapinder Kaur Research Scholar, CGC COE Landran, Mohali, India Assistant Professor, CGC COE Landran, Mohali, India Abstract 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. Index Terms: Deep Learning, Deep Neural Network, Urban Surveillance, Vehicle Classification, Vehicle Detection. © 2016 Published by MECS Publisher. Selection and/or peer review under responsibility of the Research Association of Modern Education and Computer Science. 1. Introduction 1.1. Vehicle Detection Vehicle detection is defined as detecting the vehicles on the basis of parameters such as color, shape and size. Vehicles are detected usually by extracting the vehicle queues from the satellite images. The vehicles can be detected with the help of neural network i.e. convolutional neural network. The complete system is trained in order to classify, locate and detect the objects in images. Hence this can improve the accuracy of classification, detection and localization. The network can be applied at multiple locations in the image using the sliding window technique. Then the system is trained to produce prediction of the size and location of bounding box. A technique is defined to perform object localization with convolutional network based segmentation. The central * Corresponding author. Tel.: E-mail address: