Video object detection for privacy-preserving patient monitoring in
  intensive care
Raphael Emberger Jens Michael Boss Daniel Baumann Marko Seric Shufan Huo Lukas Tuggener Emanuela Keller Thilo Stadelmann
Patient monitoring in intensive care units, although assisted by biosensors,
needs continuous supervision of staff. To reduce the burden on staff members,
IT infrastructures are built to record monitoring data and develop clinical
decision support systems. These systems, however, are vulnerable to artifacts
(e.g. muscle movement due to ongoing treatment), which are often
indistinguishable from real and potentially dangerous signals. Video recordings
could facilitate the reliable classification of biosignals using object
detection (OD) methods to find sources of unwanted artifacts. Due to privacy
restrictions, only blurred videos can be stored, which severely impairs the
possibility to detect clinically relevant events such as interventions or
changes in patient status with standard OD methods. Hence, new kinds of
approaches are necessary that exploit every kind of available information due
to the reduced information content of blurred footage and that are at the same
time easily implementable within the IT infrastructure of a normal hospital. In
this paper, we propose a new method for exploiting information in the temporal
succession of video frames. To be efficiently implementable using off-the-shelf
object detectors that comply with given hardware constraints, we repurpose the
image color channels to account for temporal consistency, leading to an
improved detection rate of the object classes. Our method outperforms a
standard YOLOv5 baseline model by +1.7% mAP@.5 while also training over ten
times faster on our proprietary dataset. We conclude that this approach has
shown effectiveness in the preliminary experiments and holds potential for more
general video OD in the future.