TITLE:
YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking
AUTHORS:
Mohamed El Imame Malaainine, Hatim Lechgar, Hassane Rhinane
KEYWORDS:
Deep Learning, YOLOv2, Vehicle Tracking, GIS
JOURNAL NAME:
Journal of Geographic Information System,
Vol.13 No.4,
July
16,
2021
ABSTRACT: The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking. In this study, YOLOv2 (You Only Look Once—version 2) is used as an open source Convolutional Neural Network (CNN), to process high-resolution satellite images, in order to generate the spatio-temporal GIS (Geographic Information System) tracks of moving vehicles. At first step, YOLOv2 is trained with a set of images of 1024 × 1024 resolution from the VEDAI database. The model showed satisfactory results, with an accuracy of 91%, and then at second step, is used to process aerial images extracted from aerial video. The output vehicle bounding boxes have been processed and fed into the GIS based LinkTheDots algorithm, allowing vehicles identification and spatio-temporal tracks generation in GIS format.