Once extracted, these features are usually saved in structured formats such as:
: For large-scale training pipelines on AWS or Google Cloud. ASL 1000 - Registry of Open Data on AWS latasha1_02mp4
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 Once extracted, these features are usually saved in
: For easy loading into Python-based models. Pose and Landmark Extraction : For easy loading
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:
: Tracking the shoulders, elbows, and wrists to define the "signing space." 2. Temporal Normalization
: 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.