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Research Manuscript
Multimodal active speaker detection and virtual cinematography for video conferencing
- Citation Author(s):
- Submitted by:
- Ross Cutler
- Last updated:
- 12 February 2020 - 12:55am
- Document Type:
- Research Manuscript
- Document Year:
- 2020
- Event:
- Presenters:
- Ross Cutler
- Paper Code:
- 5035
- Categories:
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Active speaker detection (ASD) and virtual cinematography (VC) can significantly improve the remote user experience of a video conference by automatically panning, tilting and zooming of a video conferencing camera: users subjectively rate an expert video cinematographer’s video significantly higher than unedited video. We describe a new automated ASD and VC that performs within 0.3 MOS of an expert cinematographer based on subjective ratings with a 1-5 scale. This system uses a 4K wide-FOV camera, a depth camera, and a microphone array; it extracts features from each modality and trains an ASD using an AdaBoost machine learning system that is very efficient and runs in real-time. A VC is similarly trained using machine learning to optimize the subjective quality of the overall experience. To avoid distracting the room participants and reduce switching latency the system has no moving parts – the VC works by cropping and zooming the 4K wide-FOV video stream. The system was tuned and evaluated using extensive crowdsourcing techniques and evaluated on a dataset with N=100 meetings, each 2-5 minutes in length.