Latasha1_02mp4 -

: If "latasha1_02.mp4" has missing frames or variable frame rates, use linear interpolation to fill gaps in the landmark coordinates. 3. Feature Encoding

To turn raw landmarks into a feature vector for a model (like a Transformer or LSTM), apply the following:

: 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). latasha1_02mp4

: For easy loading into Python-based models.

: Detailed mesh points to capture "non-manual markers" (facial expressions essential for ASL grammar). : If "latasha1_02

: For large-scale training pipelines on AWS or Google Cloud. ASL 1000 - Registry of Open Data on AWS

: Normalize all points relative to a "root" point (e.g., the base of the neck or center of the face) to make the features invariant to where the person is standing in the frame. For model efficiency, researchers often downsample or use

: 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