asl-recognition-0004

Use Case and High-Level Description

A human gesture recognition model for the American Sign Language (ASL) recognition scenario (word-level recognition). The model uses an S3D framework with MobileNet V3 backbone. Please refer to the MS-ASL-100 dataset specification to see the list of gestures that are recognized by this model.

The model accepts a stack of frames sampled with a constant frame rate (15 FPS) and produces a prediction on the input clip.

Example

Specification

Metric	Value
Top-1 accuracy (MS-ASL-100)	0.847
GFlops	6.660
MParams	4.133
Source framework	PyTorch*

Inputs

Image sequence, name: input, shape: 1, 3, 16, 224, 224 in the format B, C, T, H, W, where:

• B - batch size

• C - number of channels

• T - duration of input clip

• H - image height

• W - image width

Outputs

The model outputs a tensor with the shape 1, 100 in the format B, L, where:

• B - batch size

• L - logits vector for each performed ASL gestures

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

• G-API Gesture Recognition Demo

• Gesture Recognition Python\* Demo

Legal Information

[*] Other names and brands may be claimed as the property of others.