Airborne Object Tracking Dataset computer vision deep learning machine learning


Airborne Object Tracking (AOT) is a collection of 4,943 flight sequences of around 120 seconds each, collected at 10 Hz in diverse conditions. There are 5.9M+ images and 3.3M+ 2D annotations of airborne objects in the sequences. There are 3,306,350 frames without labels as they contain no airborne objects. For images with labels, there are on average 1.3 labels per image. All airborne objects in the dataset are labelled.

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  • Description
    The training dataset is further split into smaller directories. Each subdirectory contains ImageSets and Images folders. Metadata and ground truth information about image sequences are saved as groundtruth.json (and its tabular representation groundtruth.csv) in ImageSets folders. Information about airborne encounters are saved in valid_encounters_maxRange700_maxGap3_minEncLen30.json and valid_encounters_maxRange700_maxGap3_minEncLen30.csv. The Images folder holds images sampled from one sequence per directory. Images are 2448 pixels wide by 2048 pixels high, encoded as 8-bit grayscale images and saved as PNG files.
    Resource type
    S3 Bucket
    Amazon Resource Name (ARN)
    AWS Region
    AWS CLI Access (No AWS account required)
    aws s3 ls s3://airborne-obj-detection-challenge-training/ --no-sign-request
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