PCA-based surgical phases estimation with a multi-camera system

In this paper, we propose a method that automatically estimates surgical phases in a specified workflow with a multi-camera system. More specifically, our goal is to output an appropriate phase label for each one-second of input videos captured by multiple cameras in an operating room. The fundamental idea behind our work lies in constructing a hidden Markov model based on motion features, which are actually the PCA-compressed histograms of optical flow of general working contexts, including medical staff, equipment, and materials.