Latasha1_02mp4 -

: 21 points per hand to capture finger articulation and "handshape".

: Tracking the shoulders, elbows, and wrists to define the "signing space." 2. Temporal Normalization 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. : 21 points per hand to capture finger

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 To "prepare features" for this video in a

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.

: 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: