Learning-based Tracking of Fast Moving Objects

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 ba...

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Bibliographic Details
Published inarXiv.org
Main Authors Ales Zita, Sroubek, Filip
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 04.05.2020
Subjects
Algorithms
Machine learning
Moving object recognition
Segmentation
Subtraction
Tracking
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Summary: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.
ISSN:2331-8422