Authors:
Mingyang Zhang
;
Kristof Van Beeck
and
Toon Goedemé
Affiliation:
PSI-EAVISE Research Group, Department of Electrical Engineering, KU Leuven, Belgium
Keyword(s):
Synthetic Data, UAV, Object Tracking, Multimodality, Deep Learning, Siamese Network.
Abstract:
Nowadays, synthetic datasets are often used to advance the state-of-the-art in many application domains of computer vision. For these tasks, deep learning approaches are used which require vasts amounts of data. Acquiring these large annotated datasets is far from trivial, since it is very time-consuming, expensive and prone to errors during the labelling process. These synthetic datasets aim to offer solutions to the aforementioned problems. In this paper, we introduce our AirTrackSynth dataset, developed to train and evaluate deep learning models for UAV object tracking. This dataset, created using the Unreal Engine and AirSim, comprises 300GB of data in 200 well-structured video sequences. AirTrackSynth is notable for its extensive variety of objects and complex environments, setting a new standard in the field. This dataset is characterized by its multi-modal sensor data, accurate ground truth labels and a variety of environmental conditions, including distinct weather patterns,
lighting conditions, and challenging viewpoints, thereby offering a rich platform to train robust object tracking models. Through the evaluation of the SiamFC object tracking algorithm on Air-TrackSynth, we demonstrate the dataset’s ability to present substantial challenges to existing methodologies and notably highlight the importance of synthetic data, especially when the availability of real data is limited. This enhancement in algorithmic performance under diverse and complex conditions underscores the critical role of synthetic data in developing advanced tracking technologies.
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