Skip to content

Latest commit

 

History

History
90 lines (58 loc) · 2.95 KB

README.md

File metadata and controls

90 lines (58 loc) · 2.95 KB
Raw

Sign Language Recognition - using MediaPipe and DTW

License: MIT

This repository proposes an implementation of a Sign Recognition Model using the MediaPipe library for landmark extraction and Dynamic Time Warping (DTW) as a similarity metric between signs.

Source : https://www.sicara.ai/blog/sign-language-recognition-using-mediapipe

Set up

1. Open terminal and go to the Project directory

2. Install the necessary libraries

  • pip install -r requirements.txt

3. Import Videos of signs which will be considered as reference

The architecture of the videos/ folder must be:

|data/
    |-videos/
          |-Hello/
            |-<video_of_hello_1>.mp4
            |-<video_of_hello_2>.mp4
            ...
          |-Thanks/
            |-<video_of_thanks_1>.mp4
            |-<video_of_thanks_2>.mp4
            ...

To automatically create a small dataset of French signs:

  • Install ffmpeg (for MacOS brew install ffmpeg)
  • Run: python yt_download.py
  • Add more YouTube links in yt_links.csv if needed

N.B. The current dataset is insufficient to obtain good results. Feel free to add more links or import your own videos

4. Load the dataset and turn on the Webcam

  • python main.py

5. Press the "r" key to record the sign.

Code Description

Landmark extraction (MediaPipe)

  • The Holistic Model of MediaPipe allows us to extract the keypoints of the Hands, Pose and Face models. For now, the implementation only uses the Hand model to predict the sign.

Hand Model

  • In this project a HandModel has been created to define the Hand gesture at each frame. If a hand is not present we set all the positions to zero.

  • In order to be invariant to orientation and scale, the feature vector of the HandModel is a list of the angles between all the connexions of the hand.

Sign Model

  • The SignModel is created from a list of landmarks (extracted from a video)

  • For each frame, we store the feature vectors of each hand.

Sign Recorder

  • The SignRecorder class stores the HandModels of left hand and right hand for each frame when recording.
  • Once the recording is finished, it computes the DTW of the recorded sign and all the reference signs present in the dataset.
  • Finally, a voting logic is added to output a result only if the prediction confidence is higher than a threshold.

Dynamic Time Warping

  • DTW is widely used for computing time series similarity.

  • In this project, we compute the DTW of the variation of hand connexion angles over time.

References