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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.