: 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.
: 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. latasha1_02mp4
: For large-scale training pipelines on AWS or Google Cloud. ASL 1000 - Registry of Open Data on AWS
: 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). : Normalize all points relative to a "root" point (e
: If "latasha1_02.mp4" has missing frames or variable frame rates, use linear interpolation to fill gaps in the landmark coordinates. 3. Feature Encoding
: 21 points per hand to capture finger articulation and "handshape". Data Format for Training : Calculate the first
: Tracking the shoulders, elbows, and wrists to define the "signing space." 2. Temporal Normalization