Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction

• URL: http://arxiv.org/abs/2107.00422v1
• Date: Thu, 1 Jul 2021 13:08:31 GMT
• Title: Generating Synthetic Training Data for Deep Learning-Based UAV Trajectory Prediction
• Authors: Stefan Becker and Ronny Hug and Wolfgang H\"ubner and Michael Arens and Brendan T. Morris
• Abstract summary: We present an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space. 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.
• Score: 11.241614693184323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 trajectories, 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.

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