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filename
large_string
image
unknown
drone_video_1_frame_0001.jpg
[ 255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 255, 219, 0, 67, 0, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 4, 3, 2, 2, 2, 2, 5, 4, 4, 3, 4, 6, 5, 6, 6, 6, 5, 6, 6, 6, ...
drone_video_1_frame_0002.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
drone_video_1_frame_0003.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
drone_video_1_frame_0004.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
drone_video_1_frame_0005.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
drone_video_1_frame_0006.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
drone_video_1_frame_0007.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
drone_video_1_frame_0008.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
drone_video_1_frame_0009.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
drone_video_1_frame_0010.jpg
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQo(...TRUNCATED)
End of preview.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

My Videos: https://youtu.be/AgYmxM_Ynio?si=XT861nRDxgj_FhZK
https://youtu.be/Lp8Nr8ybBko?si=QT23CywUAzs590FC

Dataset: https://universe.roboflow.com/dronenotdrone/drone-detection-pq8sj I chose this dataset because it had a large amount of data to work off of and also because it was able to distinguish between drones and birds.

Kalman Filter: The filter was designed to keep track of 4 parameters: x, y, vx, and vy. x is the predicted x, y is the predicted y, and vx and vy are the values representing the percieved current velocity. It is assumed that the initial noise will be high and the filter configuration accounts for this. Besides this, the noise is assumed to be minimal and so few adjustments besides this were deemed necessary.

Failure Cases: In the case of a missed detection, the tracker ignores the missed detection and all of the data that comes with it, allowing only the correct data to influence decision making.

Parquet Format: columns are Filename, image. Filename is the original file name of the image. "image" is the image in the form of bytes.

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