Automatic Vehicle Counting and Tracking in Aerial Video Feeds using Cascade Region-based Convolutional Neural Networks and Feature Pyramid Networks

Abstract

Unmanned aerial vehicles, or drones, are poised to solve many problems associated with data collection in complex urban environments. Drones are easy to deploy, have a great ability to move and explore the environment, and are relatively cheaper than other data collection methods. This study investigated the use of Cascade Region-based convolutional neural network (R-CNN) networks to enable automatic vehicle counting and tracking in aerial video streams.The presented technique combines feature pyramid networks and a Cascade R-CNN architecture to enable accurate detection and classification of vehicles.The paper discusses the implementation and evaluation of the detection and tracking techniques and highlights their advantages when they are used to collect traffic data.