A network of video cameras melded a unique algorithm let scientists with the Carnegie Mellon University track the locations of multiple individuals in complex, indoor setting - just like Harry Potter's Marauder's Map.
If you haven't seen it, the Harry Potter Wiki describes the Marauder's Map as "a magical document that reveals all of Hogwarts School of Witchcraft and Wizardry. Not only does it show every classroom, every hallway, and every corner of the castle, but it also shows every inch of the grounds, as well as all the secret passages that are hidden within its walls and the location of every person in the grounds, portrayed by a dot. It is also capable of accurately identifying each person...even the Hogwarts ghosts are not exempt from this."
The Carnegie Mellon system was able to automatically follow the movements of 13 people within a nursing home, even though individuals sometimes slipped out of view of the cameras and researchers said they made use of multiple cues from the video feed: apparel color, person detection, trajectory and, perhaps most significantly, facial recognition, according to university researchers.
Specifically, the Carnegie Mellon algorithm significantly improved on two of the leading algorithms in multi-camera, multi-object tracking. It located individuals within one meter of their actual position 88% of the time, compared with 35% and 56% for the other algorithms, researchers said.
Multi-camera, multi-object tracking has been an active field of research for a decade, but automated techniques have only focused on well-controlled lab environments. The Carnegie Mellon team, by contrast, proved their technique with actual residents and employees in a nursing facility-with camera views compromised by long hallways, doorways, people mingling in the hallways, variations in lighting and too few cameras to provide comprehensive, overlapping views, the researchers stated.
The Carnegie Mellon researchers said they developed their tracking technology as part of an effort to monitor the health of nursing home residents but automated tracking techniques also would be useful in airports, public facilities and other areas where security is a concern. Despite the importance of cameras in identifying perpetrators following this spring's Boston Marathon bombing and the 2005 London bombings, much of the video analysis necessary for tracking people continues to be done manually, said Alexander Hauptmann, principal systems scientist in the Carnegie Computer Science Department.
The researchers said such motion tracking systems have a number of challenges. For example, something as simple as tracking based on color of clothing proved difficult because the same color apparel can appear different to cameras in different locations, depending on variations in lighting. Likewise, a camera's view of an individual can often be blocked by other people passing in hallways, by furniture and when an individual enters a room or other area not covered by cameras, so individuals must be regularly re-identified by the system.
"Face detection helps immensely in re-identifying individuals on different cameras. But that faces can be recognized in less than 10% of the video frames. So the researchers developed mathematical models that let them combine information, such as appearance, facial recognition and motion trajectories," the researchers stated. "Using all of the information is key to the tracking process, but facial recognition proved to be the greatest help. When the researchers removed facial recognition information from the mix, their on-track performance in the nursing home data dropped from 88% to 58%, not much better than one of the existing tracking algorithms."
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