Author(s)

Mohamed El Imame Malaainine¹, Hatim Lechgar², Hassane Rhinane²

¹Hassania School of Public Works, Casablanca, Morocco.
²FSAC-Hassan II University, Casablanca, Morocco.

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.

Deep Learning, YOLOv2, Vehicle Tracking, GIS

Malaainine, M. , Lechgar, H. and Rhinane, H. (2021) YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking. Journal of Geographic Information System, 13, 395-409. doi: 10.4236/jgis.2021.134022.