Dataset for benchmarking Multiple Object Tracking and Segmentation (MOTS) in an apple orchard field.
A dataset of temporally consistent apple images and labels taken using UAVs and a wearable sensor in an orchard, consisting of 86000 manually annotated apple instances and 1700 frames annotated in the MOTS (Multi-object Tracking and Segmentation) style.
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Main Authors: | , , |
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Format: | Dataset biblioteca |
Published: |
Wageningen University & Research
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Subjects: | MOTS, computer vision, deep learning, precision agriculture, yield estimation, |
Online Access: | https://research.wur.nl/en/datasets/dataset-for-benchmarking-multiple-object-tracking-and-segmentatio |
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Summary: | A dataset of temporally consistent apple images and labels taken using UAVs and a wearable sensor in an orchard, consisting of 86000 manually annotated apple instances and 1700 frames annotated in the MOTS (Multi-object Tracking and Segmentation) style. |
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