: If you are using raw video instead of just landmarks, extract Optical Flow features to track the motion intensity between frames. 4. Data Format for Training
The ASL 1000 dataset is pre-annotated with 2D landmarks, but for custom feature preparation, you can use frameworks like MediaPipe or OpenPose to generate:
: ASL videos are often recorded at 30 or 60 FPS. For model efficiency, researchers often downsample or use fixed-length sequences (e.g., taking 32 or 64 frames per clip).
: Calculate the first and second derivatives of the landmark coordinates to capture the speed and fluidity of the signs.
: If you are using raw video instead of just landmarks, extract Optical Flow features to track the motion intensity between frames. 4. Data Format for Training
The ASL 1000 dataset is pre-annotated with 2D landmarks, but for custom feature preparation, you can use frameworks like MediaPipe or OpenPose to generate:
: ASL videos are often recorded at 30 or 60 FPS. For model efficiency, researchers often downsample or use fixed-length sequences (e.g., taking 32 or 64 frames per clip).
: Calculate the first and second derivatives of the landmark coordinates to capture the speed and fluidity of the signs.
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