: Tracking the shoulders, elbows, and wrists to define the "signing space." 2. Temporal Normalization
: 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
: 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. : Tracking the shoulders, elbows, and wrists to
: If "latasha1_02.mp4" has missing frames or variable frame rates, use linear interpolation to fill gaps in the landmark coordinates. 3. Feature Encoding : Normalize all points relative to a "root" point (e
: Calculate the first and second derivatives of the landmark coordinates to capture the speed and fluidity of the signs.
To turn raw landmarks into a feature vector for a model (like a Transformer or LSTM), apply the following:
To "prepare features" for this video in a machine learning or computer vision context, you should focus on extracting . Below is a breakdown of the standard features typically extracted for this specific dataset: 1. Pose and Landmark Extraction