Video techniques enable tracking of multiple people indoors

The method was able to automatically follow the movements of 13 people within a nursing home, even though individuals sometimes moved out of view of the cameras. To track them, the researchers made use of multiple cues (apparel colour, person detection, trajectory and facial recognition) from the video feed.

The Carnegie Mellon team proved their technique with residents and employees in a nursing facility where camera views were 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 is claimed to have significantly improved on two of the leading algorithms in multi-camera, multi-object tracking. It located individuals within one metre of their actual position 88 per cent of the time, compared with 35 per cent and 56 per cent for the other algorithms.

The researchers - Alexander Hauptmann, principal systems scientist in the Computer Science Department (CSD); Shoou-I Yu, a Ph.D. student in the Language Technologies Institute; and Yi Yang, a CSD post-doctoral researcher - will present their findings June 27 at the Computer Vision and Pattern Recognition Conference in Portland, Oregon.