Muster Point Manifest Vision System
During an emergency evacuation situation, knowledge of personnel locations is required for efficient evacuation and deployment of rescue vehicles.
Knowing who has boarded a lifeboat or raft vs. who may still be on board the rig in a timely manner can save lives. It makes sense to leverage camera technologies to track and identify people as they enter muster areas (or lifeboats or rafts), and automatically generate passenger manifest/muster lists. In the case of an emergency, these manifests can be immediately and automatically sent ashore, with a listing of personnel, a photo of them boarding the lifeboat or raft and their company ID photo for comparison, for further analysis.
This video shows an example of the technology being used to identify people as they enter an enclosed space. As each person enters the scene, face detection algorithms are applied to isolate the subject’s face, and then machine learning and pattern recognition techniques are used to match the face to a pre-existing database of faces (e.g., from ID photos, or photographs taken when people arrive on the rig). This information can then be used to generate a passenger manifest.
Even when personnel identity is unknown, or that data is protected, computer vision systems can count unique faces as they enter a scene. Thus, a muster count could be generated, which can be double-checked against manual rosters. Our personnel counting system is robust against double-counting when faces exit and re-enter a scene, when they are partially occluded or fully occluded. Location and anonymized numeral ID of each unique face is tracked through the scene