Carnegie Mellon University has developed a method for tracking the locations of multiple individuals in complex, indoor settings using a network of video cameras. The method 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. The researchers made use of multiple cues from the video feed: apparel color, person detection, trajectory and, perhaps most significantly, facial recognition.

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 performance of 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 percent of the time, compared with 35 percent and 56 percent for the other algorithms. These automated tracking techniques also would be useful in airports, public facilities and other areas where security is a concern.

Something as simple as tracking based on color of clothing proved difficult, for instance, 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.

Further work will be necessary to extend the technique during longer periods of time and enable real-time monitoring. The researchers also are looking at additional ways to use video to monitor resident activity while preserving privacy, such as by only recording the outlines of people together with distance information from depth cameras similar to the Microsoft Kinect.