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Update description of detection training sequences.
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wjbeksi committed Jan 23, 2024
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Expand Up @@ -192,18 +192,17 @@ <h2 class="title is-3">Dataset</h2>
sequences for training and testing multi-object tracking methods.
Each tracking sequence is 10 to 20 seconds in length. The dataset
contains of a total of 30 sequences of which 17 are for training
and the remaining 13 are for testing. In addition, two of the
training sequences consist of roughly 5,000 annotated images, which
can be used to train a cotton boll detection model. The video
sequences were captured at 4K resolution and at distinct frame
rates (e.g., 10, 15, 30). There are typically 2 to 10 cotton bolls
per cluster. The average width and height of an annotated bounding
box is approximately 230 x 210 pixels. To make the dataset robust
to environmental conditions, we recorded the field videos at
separate times of day to account for varying lighting conditions.
In total, there are roughly 30 x 300 frames with 150,000 labeled
instances. On average there are 70 unique cotton bolls in each
sequence.
and the remaining 13 are for testing. Among the training sequences,
two of them consist of roughly 5,000 annotated images, which can be
used to train a cotton boll detection model. The video sequences
were captured at 4K resolution and at distinct frame rates (e.g.,
10, 15, 30). There are typically 2 to 10 cotton bolls per cluster.
The average width and height of an annotated bounding box is
approximately 230 x 210 pixels. To make the dataset robust to
environmental conditions, we recorded the field videos at separate
times of day to account for varying lighting conditions. In total,
there are roughly 30 x 300 frames with 150,000 labeled instances.
On average there are 70 unique cotton bolls in each sequence.
</p>
</div>
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