Cardio-Pulmonary Resuscitation (CPR) Scene Retrieval from Medical Simulation Videos Using Local Binary Patterns Over Three Orthogonal Planes

We present a framework to detect and retrieve CPR activity scenes from medical simulation videos. Medical simulation is a modern training method for medical practitioners, where an emergency patient condition is simulated on humanlike mannequins and the students act upon. These simulation sessions are recorded for later debriefing. With the increasing number of simulation videos, automatic detection and retrieval of specific scenes became necessary. In this paper, we present an automated CPR scene retrieval system which can identify and retrieve CPR activity scenes, using Local Binary Patterns Over Three Orthogonal Planes (LBP-TOP) features. We present a comparative study of LBP-TOP features and Histogram of Orientation of Gradients (HOG3D) features for CPR action detection. We also present the results of decision level fusion of different classifier outputs. Promising classification results have been achieved, suggesting the proposed technique to be effective in detecting and retrieving CPR activity scenes from simulation videos.