- The TexCot22 dataset is a set of cotton crop - video 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 28 sequences of which 15 are - for training and the remaining 13 are for testing. Additionally, - around 5,000 images containing several cotton bolls along with - their bounding box annotations in each image are provided to - train a cotton boll detector. The video + The TexCot22 dataset is a set of cotton crop video + 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. + 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.