- The paper introduces a real-time method for detecting sign language activity using optical flow features extracted from human pose estimation.
- It leverages OpenPose for full-body pose estimation and a single-layer LSTM, achieving 87-91% accuracy with less than 4ms per frame inference speed on a CPU.
- The proposed method has significant practical implications for improving accessibility for sign language users in videoconferencing applications like Google Meet and Zoom.
Real-Time Sign Language Detection Using Human Pose Estimation
The paper presents an innovative approach to real-time sign language detection utilizing human pose estimation techniques. It addresses the specific challenge of ensuring sign language users appropriate attention in videoconferencing settings, a task that has grown more crucial with the rise of virtual meetings. The authors introduce a lightweight, efficient model aimed at detecting sign language by analyzing optical flow features derived from human pose estimations.
Methodology
At the core of the proposed method is the extraction of meaningful optical flow features from video frames. These features are processed through a linear classifier and a recurrent model to determine whether a frame contains signing activity. This is distinct from existing approaches to sign language recognition, which usually focus on interpreting the meaning of signs, or identification, which attempts to determine the specific sign language being used.
The paper leverages full-body pose estimation, facilitated by OpenPose, to track and calculate the movement of key landmarks, such as joints and facial features, on the human body. Temporal dynamics are captured using a single-layer LSTM, which assists in recognizing signing activity over time. The evaluated system achieved an accuracy range of 87% to 91%, with a notable improvement reaching 91% using a recurrent model, while maintaining an inference speed under 4 milliseconds per frame.
Results
Empirical evaluation was conducted using the Public DGS Corpus, encapsulating extensive footage of conversations in German Sign Language and annotated for sign language activity. The results are noteworthy, not only for high accuracy but also for the real-time performance executed on ordinary CPUs, which is crucial for deployment in consumer-facing applications. The paper reports a wide range of input experimentation, including pose estimation and bounding boxes, comparing their efficiency and predictive accuracy.
Implications and Future Research
The practical implications of this work are significant, offering a viable solution for accommodating sign language users in videoconferencing platforms. By embedding such detection systems into applications like Google Meet and Zoom, the accessibility of these platforms could be drastically improved, providing sign language users a more equitable virtual communication environment.
From a theoretical perspective, this research opens avenues for further exploration in human movement detection, sensor integration for improving real-time accuracy, and optimizing pose estimation models for quicker computations. While the current approach focuses on binary classification of sign presence, future research could delve into more granular detection, perhaps incorporating sign recognition or sign language identification for more nuanced applications.
Conclusion
This paper offers a compelling contribution to the field of sign language processing, proposing a robust and swift method to serve the immediate needs of sign language users in digital communication spaces. It lays a foundation for subsequent improvements and integrations into broader applications, paving the way for more inclusive technology environments. While the paper effectively demonstrates the feasibility and utility of optical flow features for sign detection, it encourages further work to refine and extend these methodologies into comprehensive sign language interfaces.