Trajectory recovery for a number of people in a room is accomplished by mounting overhead cameras in a grid pattern around the space such that the entire room is in view. Some overlap is left between adjacent cameras to minimize boundary errors. Frame capture occurs at a low-level after which we perform Bayer pattern demosaicing and a calibration step to minimize tracking errors due to lens distortion. A logging mechanism is used to ensure that frames from each camera are correctly time-stamped.

To facilitate person identification, people in the space are equipped with uniquely identifiable tags that are worn on a hat. This allows us to capture head orientation and position over time using the Augmented Reality Toolkit to perform tag finding and pose estimation. The library gives us a transformation allowing us to calculate position and yaw, pitch and roll angles for each tag present in each camera image. These measurements are relatively stable but can become quite noisy under certain lighting conditions and at the frame boundaries. To minimize the effects of noise we use a particle filter to further refine the estimate as well as integrate the measurements from all the cameras given a motion model that approximates possible human motions over each time step.

This work is supported in part by the NSF grants IIS-0803565, CNS-0709296, and in part by the Nancy Laurie Marks Family Foundation.

David Feil-Seifer
Aaron St. Clair