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:
: If "latasha1_02.mp4" has missing frames or variable frame rates, use linear interpolation to fill gaps in the landmark coordinates. 3. Feature Encoding
The file appears to be a specific clip from the ASL 1000 Dataset , a high-fidelity collection of American Sign Language (ASL) videos designed for research in gesture analysis and sign recognition. latasha1_02mp4
: 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
: Calculate the first and second derivatives of the landmark coordinates to capture the speed and fluidity of the signs. The ASL 1000 dataset is pre-annotated with 2D
: 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.
Once extracted, these features are usually saved in structured formats such as: : If you are using raw video instead
: For large-scale training pipelines on AWS or Google Cloud. ASL 1000 - Registry of Open Data on AWS