Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles
| Autor: | Friedrich Kruber, Eduardo Sánchez Morales, Michael Botsch, Samarjit Chakraborty |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Data processing Source code Artificial neural network Computer science business.industry media_common.quotation_subject Computer Vision and Pattern Recognition (cs.CV) 0211 other engineering and technologies Process (computing) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Drone Object detection Position (vector) 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Computer vision Artificial intelligence business 021101 geological & geomatics engineering media_common |
| Popis: | The availability of real-world data is a key element for novel developments in the fields of automotive and traffic research. Aerial imagery has the major advantage of recording multiple objects simultaneously and overcomes limitations such as occlusions. However, there are only few data sets available. This work describes a process to estimate a precise vehicle position from aerial imagery. A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose. Two training data sets are employed: The first one is optimized for detecting the test vehicle, while the second one consists of randomly selected images recorded on public roads. To reduce errors, several aspects are accounted for, such as the drone movement and the perspective projection from a photograph. The estimated position is comapared with a reference system installed in the test vehicle. It is shown, that a mean accuracy of 20 cm can be achieved with flight altitudes up to 100 m, Full-HD resolution and a frame-by-frame detection. A reliable position estimation is the basis for further data processing, such as obtaining additional vehicle state variables. The source code, training weights, labeled data and example videos are made publicly available. This supports researchers to create new traffic data sets with specific local conditions. Copyright 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works |
| Databáze: | OpenAIRE |
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