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Dataset
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.