Reunite is a system that utilises crowdsourcing and machine learning techniques to help reunite those separated by conflict and natural disaster.
Imagine the following scenario. A disaster occurs in a remote part of the developing world. The local population are forced to flee their homes. Many are separated from their family and friends. With no mobile or Internet communication, finding loved ones in the aftermath of a disaster is incredibly difficult. Relief organisations go to great lengths to help people find those they are missing. Our system aims to make this process easier, faster and more secure.
We envisage a system were relief workers can to go into the remotest areas of the world with a video phone and securely record video interviews with those who have lost loved ones. These interviews are uploaded to the Reunite system where they are analysed by a global network of trusted individuals. Potential matches are reported back to relief workers, who can inform the relevant individuals and help bring them back together.
In particular, this project aims to demonstrate the feasibility of;
- capturing a video/audio interview with a person (such as a refugee or an IDP) using a mobile device;
- having these interviews analysed by a “crowd” of individuals and
- having a crowd match together individuals who may be looking for each other.
Machine Learning techniques will be used to ensure that the tasks carried out by the crowd are performed reliably; identifying and championing capable users and ignoring those who are less capable.
The system is being developed within the Machine Learning and Optimisation group at The University of Manchester by Peter Sutton and Lloyd Henning, under the supervision of Dr. Gavin Brown. The project is financed by the University’s EPSRC KTA scheme.