Learning-based Tracking of Fast Moving Objects

• URL: http://arxiv.org/abs/2005.01802v1
• Date: Mon, 4 May 2020 19:20:09 GMT
• Title: Learning-based Tracking of Fast Moving Objects
• Authors: Ales Zita, Filip Sroubek
• Abstract summary: Tracking fast moving objects, which appear as blurred streaks in video sequences, is a difficult task for standard trackers. 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.
• Score: 8.8456602191903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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