Vehicle Detection in Traffic Monitoring

Abstract

Traffic Monitoring is a challenging task on crowded roads. Traffic Monitoring procedures are manual, expensive, time consuming and involve human operators. Large-scale storage and analysis of video streams were not possible due to limited availability. However, it is now possible to implement object detection and tracking, behavioral analysis of traffic patterns, number plate recognition and surveillance on video streams produced by traffic monitoring. In Big data, video datasets are so large that typical database systems are not able to store and analyses the datasets. Storing and Processing big volumes of data requires Scalability, Fault Tolerance and Availability. Thus, Big Data and Cloud computing are two compatible concepts as cloud enables big data for traffic monitoring. In this paper, we proposed vehicle detection for traffic monitoring. We have applied Support Vector Machine (SVM) machine learning algorithm for detecting vehicles.