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.
Highlights
Vehicles tracking is an important subject with interesting applications
While classical methods of vehicle tracking are based on the combination of GPS, GSM, GPRS and internet technologies [8] [9] [10] [11], new methods based on imagery and AI are rapidly evolving [12]-[17]. the advantage of these new methods is their ability to process data at large scales, without the need to first install special equipment in tracked vehicles; They take advantage of accelerated advances in artificial intelligence, especially deep learning, and significantly reduce the cost of access to these analysis data for the largest number of interested researchers and businesses
To solve the problem of handling continuous aerial video stream, which represents a big technical challenge [43], the video stream is converted into a series of images, with a suitable resolution for the trained YOLOv2 algorithm
Summary
Vehicles tracking is an important subject with interesting applications It has been extensively studied from different angles, using both classical methods of traditional object detection and GIS methods, based on GPS and real time communications tools. As one of the most important tasks in computer vision, object detection is rapidly growing, thanks to the latest advances in deep learning based methods and computational power with clusters of graphics processing units (GPUs). This offers new opportunities for vehicle tracking, through the use of high-resolution satellite imagery and deep learning methods, based on Convolutional Neural.
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