Uploaded May 2021 | Updated September 2026, 2 hours ago
Millimeter wave (mmWave) Doppler radar is a new and promising sensing approach for human activity recognition, offering signal richness approaching that of microphones and cameras, but without many of the privacy-invading downsides. However, unlike audio and computer vision approaches that can draw from huge libraries of videos for training deep learning models, Doppler radar has no existing large datasets, holding back this otherwise promising sensing modality. In response, we set out to create a software pipeline that converts videos of human activities into realistic, synthetic Doppler radar data. We show how this cross-domain translation can be successful through a series of experimental results. Overall, we believe our approach is an important stepping stone towards significantly reducing the burden of training such as human sensing systems, and could help bootstrap uses in human-computer interaction.
Learn more: karan-ahuja.com/vid2dop.html
Citation:
Karan Ahuja, Yue Jiang, Mayank Goel, and Chris Harrison. 2021. Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 292, 1–10. DOI:doi.org/10.1145/3411764.3445138
Millimeter wave (mmWave) Doppler radar is a new and promising sensing approach for human activity recognition, offering signal richness approaching that of microphones and cameras, but without many of the privacy-invading downsides. However, unlike audio and computer vision approaches that can draw from huge libraries of videos for training deep learning models, Doppler radar has no existing large datasets, holding back this otherwise promising sensing modality. In response, we set out to create a software pipeline that converts videos of human activities into realistic, synthetic Doppler radar data. We show how this cross-domain translation can be successful through a series of experimental results. Overall, we believe our approach is an important stepping stone towards significantly reducing the burden of training such as human sensing systems, and could help bootstrap uses in human-computer interaction.
Learn more: karan-ahuja.com/vid2dop.html
Citation:
Karan Ahuja, Yue Jiang, Mayank Goel, and Chris Harrison. 2021. Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 292, 1–10. DOI:doi.org/10.1145/3411764.3445138
