Robust operating room video anonymization based on ensemble deep learning

Abstract

Disclosed are various face-detection and human de-identification systems and techniques based on deep learning. In one aspect, a process for de-identifying people captured in an operating room (OR) video is disclosed. This process can begin by receiving a sequence of video frames from an OR video. Next, the process applies a first machine-learning face detector based on a first deep-learning model to each video frame in the sequence of video frames to generate a first set of detected faces. The process further applies a second machine-learning face detector to the sequence of video frames to generate a second set of detected faces, wherein the second machine-learning face detector is constructed based on a second deep-learning model different from the first deep-learning model. The process subsequently de-identifies the received sequence of video frames by blurring out both the first set of detected faces and the second set of detected faces.