: Tracking the shoulders, elbows, and wrists to define the "signing space." 2. Temporal Normalization

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:

: For easy loading into Python-based models.

: 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

Latasha1_02mp4 Apr 2026

: Tracking the shoulders, elbows, and wrists to define the "signing space." 2. Temporal Normalization

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: latasha1_02mp4

: For easy loading into Python-based models. : Tracking the shoulders, elbows, and wrists to

: 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 : Tracking the shoulders