Document Type
Article
Publication Date
10-27-2021
Publication Title
Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems (ROBOVIS 2021)
Publisher
Institute for Systems and Technologies of Information, Control and Communication
Publisher Location
Virtual
Volume
1
First page number:
13
Last page number:
21
Abstract
Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are increasingly replacing classic approaches in object tracking applications for motion prediction. On the one hand, these models can capture complex object dynamics with less modeling required, but on the other hand, they depend on a large amount of training data for parameter tuning. Towards this end, we present an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space. Since UAVs, or rather quadrotors are dynamical systems, they can not follow arbitrary trajectories. With the prerequisite that UAV trajectories fulfill a smoothness criterion corresponding to a minimal change of higher-order motion, methods for planning aggressive quadrotors flights can be utilized to generate optimal trajectories through a sequence of 3D waypoints. By projecting these maneuver traject ories, which are suitable for controlling quadrotors, to image space, a versatile trajectory data set is realized. To demonstrate the applicability of the synthetic trajectory data, we show that an RNN-based prediction model solely trained on the generated data can outperform classic reference models on a real-world UAV tracking dataset. The evaluation is done on the publicly available ANTI-UAV dataset.
Keywords
Unmanned-Aerial-Vehicle (UAV); Synthetic Data Generation; Trajectory Prediction; Deep-learning; Recurrent Neural Networks (RNNs); Training Data; Quadrotors
Disciplines
Artificial Intelligence and Robotics | Robotics
File Format
File Size
6200 KB
Language
English
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Repository Citation
Morris, B. T.,
Becker, S.,
Hug, R.,
Huebner, W.,
Arens, M.
(2021).
Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction.
Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems (ROBOVIS 2021), 1
13-21.
Virtual: Institute for Systems and Technologies of Information, Control and Communication.
http://dx.doi.org/10.5220/0010621400003061