IMPROVED TRAFFIC CONTROL SYSTEM THROUGH DETECTION AND CLASSIFICATION OF VEHICLES USING CAMERA BASED CROWD SENSING WITH DEEP LEARNING APPROACH.

Abstract

Abstract - Intelligent traffic light control system can be considered as one of the main aspects of a smart city. There is significant increase in the number of People that own vehicles and this leads to traffic congestion in urban cities of developing countries. Traffic congestion is one of the highly challenging problems people face in their daily lives which affect their working productivity, causes accident, unnecessary delays and possibly loss of lives. The control of traffics based on the densities and classes of the vehicles using deep learning approach proved to be a better approach in handling traffic jams according to the current state of the studies. However, the current state of the studies did not have special consideration for the class of special vehicles (VIPs) which need special consideration due to their importance and tight schedules. Also, it did not consider allocating time based on the classification. Thus, a model to improve the detection and classification of vehicles on side of intersection as well as special consideration for VIPs was proposed. The proposed model was built using Faster R-CNN based on Tensorflow machine learning framework on a Google Colab environment. The model was able to detect and classify vehicles as “special vehicles (i.e. VIP), Emergency (EV) and Others” classes of vehicles, a time allocation simulator was proposed based on the specified classes with special timing for VIPs. Results show that using the proposed method, 94.43% accuracy was achieved.