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 background subtraction with static background and slow deblurring algorithms. In this paper, we present a tracking-bysegmentation approach implemented using state-of-the-art deep learning methods that performs near-realtime tracking on realworld 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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Aiheet: | video matemaattis-looginen malli reaaliaikainen käsittely ohjelmisto pöytätennis tennis sulkapallo lentopallo rantalentopallo baseball liikkeen kuvaaminen |
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Aihealueet: | tekniset ja luonnontieteet urheilukilpailut |
Tagging: | deep learning künstliche Intelligenz |
Julkaisussa: | arXiv e-print repository |
Julkaistu: |
2020
|
Numero: | preprint |
Sivuja: | 1-7 |
Julkaisutyypit: | artikkeli |
Kieli: | englanti (kieli) |
Taso: | kehittynyt |