Learning-based Tracking of Fast Moving Objects

Ales Zita; Filip Sroubek

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arXiv · 2005.01802

Learning-based Tracking of Fast Moving Objects

Ales Zita·Filip Sroubek

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Abstract

Tracking fast moving objects, which appear as blurred streaks in video sequences, is a difficult task for standard trackers as the object position does not overlap in consecutive video frames and texture information of the objects is blurred. Up-to-date approaches tuned for this task are based on background subtraction with static background and slow deblurring algorithms. In this paper, we present a tracking-by-segmentation approach implemented using state-of-the-art deep learning methods that performs near-realtime tracking on real-world video sequences. We implemented a physically plausible FMO sequence generator to be a robust foundation for our training pipeline and demonstrate the ease of fast generator and network adaptation for different FMO scenarios in terms of foreground variations.

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Ales Zita, Filip Sroubek. 2020-05-04. Learning-based Tracking of Fast Moving Objects. https://arxiv.org/abs/2005.01802

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