Traffic Density Monitoring Using Tensorflow

EasyChair Preprint no. 10596

Abstract

The rapid growth of urban areas and the increasing number of vehicles on the roads have led to significant challenges in traffic management and monitoring. This research proposes an intelligent traffic monitoring system that utilizes deep learning techniques to analyze video input and extract valuable information about different types of vehicles present in the traffic video input. This system employs TensorFlow, Convolution Neural Network (CNN) for object detection and classification, enabling accurate identification of cars, buses, and trucks. The detected objects are then tracked across frames to provide real-time information about their movement and behavior, hence dynamic management of waiting time can be achieved.

Keyphrases: Classification, CNN, deep learning, Intelligent traffic monitoring, object detection, TensorFlow, traffic patterns, Web-based Visualisation

@Booklet{EasyChair:10596,
  author = {Akshantula Neha and M Raghavi and M Madhurashree and M M Ranjitha and V K Annapurna},
  title = {Traffic Density Monitoring Using Tensorflow},
  howpublished = {EasyChair Preprint no. 10596},

  year = {EasyChair, 2023}}