Video conferencing should be accessible to everyone, including users who communicate using sign language. However, since most video conference applications transition window focus to those who speak aloud, it makes it difficult for signers to “get the floor” so they can communicate easily and effectively. Enabling real-time sign language detection in video conferencing is challenging, since applications need to perform classification using the high-volume video feed as the input, which makes the task computationally heavy. In part, due to these challenges, there is only limited research on sign language detection.
In “Real-Time Sign Language Detection using Human Pose Estimation”, presented at SLRTP2020 and demoed at ECCV2020, we present a real-time sign language detection model and demonstrate how it can be used to provide video conferencing systems a mechanism to identify the person signing as the active speaker.
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| Maayan Gazuli, an Israeli Sign Language interpreter, demonstrates the sign language detection system. |
Our Model
To enable a real-time working solution for a variety of video conferencing applications, we needed to design a light weight model that would be simple to “plug and play.” Previous attempts to integrate models for video conferencing applications on the client side demonstrated the importance of a light-weight model that consumes fewer CPU cycles in order to minimize the effect on call quality. To reduce the input dimensionality, we isolated the information the model needs from the video in order to perform the classification of every frame.
Because sign language involves the user’s body and hands, we start by running a pose estimation model, PoseNet. This reduces the input considerably from an entire HD image to a small set of landmarks on the user’s body, including the eyes, nose, shoulders, hands, etc. We use these landmarks to calculate the frame-to-frame optical flow, which quantifies user motion for use by the model without retaining user-specific information. Each pose is normalized by the width of the person’s shoulders in order to ensure that the model attends to the person signing over a range of distances from the camera. The optical flow is then normalized by the video’s frame rate before being passed to the model.
To test this approach, we used the German Sign Language corpus (DGS), which contains long videos of people signing, and includes span annotations that indicate in which frames signing is taking place. As a naïve baseline, we trained a linear regression model to predict when a person is signing using optical flow data. This baseline reached around 80% accuracy, using only ~3μs (0.000003 seconds) of processing time per frame. By including the 50 previous frames’ optical flow as context to the linear model, it is able to reach 83.4%.
To generalize the use of context, we used a long-short-term memory (LSTM) architecture, which contains memory over the previous timesteps, but no lookback. Using a single layer LSTM, followed by a linear layer, the model achieves up to 91.5% accuracy, with 3.5ms (0.0035 seconds) of processing time per frame.
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| Classification model architecture. (1) Extract poses from each frame; (2) calculate the optical flow from every two consecutive frames; (3) feed through an LSTM; and (4) classify class. |
Proof of Concept
Once we had a functioning sign language detection model, we needed to devise a way to use it for triggering the active speaker function in video conferencing applications. We developed a lightweight, real-time, sign language detection web demo that connects to various video conferencing applications and can set the user as the “speaker” when they sign. This demo leverages PoseNet fast human pose estimation and sign language detection models running in the browser using tf.js, which enables it to work reliably in real-time.
When the sign language detection model determines that a user is signing, it passes an ultrasonic audio tone through a virtual audio cable, which can be detected by any video conferencing application as if the signing user is “speaking.” The audio is transmitted at 20kHz, which is normally outside the hearing range for humans. Because video conferencing applications usually detect the audio “volume” as talking rather than only detecting speech, this fools the application into thinking the user is speaking.
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| The sign language detection demo takes the webcam’s video feed as input, and transmits audio through a virtual microphone when it detects that the user is signing. |
You can try our experimental demo right now! By default, the demo acts as a sign language detector. The training code and models as well as the web demo source code is available on GitHub.
Demo
In the following video, we demonstrate how the model might be used. Notice the yellow chart at the top left corner, which reflects the model’s confidence in detecting that activity is indeed sign language. When the user signs, the chart values rise to nearly 100, and when she stops signing, it falls to zero. This process happens in real-time, at 30 frames per second, the maximum frame rate of the camera used.
| Maayan Gazuli, an Israeli Sign Language interpreter, demonstrates the sign language detection demo. |
User Feedback
To better understand how well the demo works in practice, we conducted a user experience study in which participants were asked to use our experimental demo during a video conference and to communicate via sign language as usual. They were also asked to sign over each other, and over speaking participants to test the speaker switching behavior. Participants responded positively that sign language was being detected and treated as audible speech, and that the demo successfully identified the signing attendee and triggered the conferencing system’s audio meter icon to draw focus to the signing attendee.
Conclusions
We believe video conferencing applications should be accessible to everyone and hope this work is a meaningful step in this direction. We have demonstrated how our model could be leveraged to empower signers to use video conferencing more conveniently.
Acknowledgements
Amit Moryossef, Ioannis Tsochantaridis, Roee Aharoni, Sarah Ebling, Annette Rios, Srini Narayanan, George Sung, Jonathan Baccash, Aidan Bryant, Pavithra Ramasamy and Maayan Gazuli
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