Large networks of cameras are increasingly deployed in public places like airports, railway stations, college campuses and office buildings. These cameras typically span large geospatial areas and have non-overlapping fields-of-views (FOVs) to provide enhanced coverage. Such networks provide huge amounts of video data, which is either manually monitored by law enforcement officers or utilized after the fact for forensic purposes. Human monitoring of these videos is erroneous, time consuming and expensive, thereby severely reducing the effectiveness of surveillance. Automated analysis of large amounts of video data can not only process the data faster but significantly improve the quality of surveillance. Video analysis can enable long term activity and behavior characterization of people in a scene. Such analysis is required for high-level surveillance tasks like suspicious activity detection or undesirable event prediction for timely alerts to security personnel making surveillance more pro-active.
Understanding of a surveillance scene through computer vision requires the ability to track people across multiple cameras, perform crowd movement analysis and activity detection. Tracking people across multiple cameras is essential for wide area scene analytics and person re-identification is a fundamental aspect of multi-camera tracking. Re-identification (Re-ID) is defined as a process of establishing correspondence between images of a person taken from different cameras. It is used to determine whether instances captured by different cameras belong to the same person, in other words, assign a stable ID to different instances of the person.