Object tracking in the presence of occlusions using multiple cameras
Abstract
This article describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication bandwidth and energy, we combine a task-driven approach with camera subset selection. In the task-driven approach, each camera first performs simple local processing to detect the horizontal position of the object in the image. This information is then sent to a cluster head to track the object. We assume the locations of the static occluders to be known, but only prior statistics on the positions of the moving occluders are available. A noisy perspective camera measurement model is introduced, where occlusions are captured through occlusion indicator functions. An auxiliary particle filter that incorporates the occluder information is used to track the object. The camera subset selection algorithm uses the minimum mean square error of the best linear estimate of the object position as a metric, and tracking is performed using only the selected subset of cameras.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 01, 2013
- Source ID
- 10.1145/2422966.2422973
Entities
People
- Abbas El Gamal
- Ali O. Ercan
- Leonidas J. Guibas
Organizations
- Army Research Office
- Defense Advanced Research Projects Agency
- Division of Computer and Network Systems
- Office of Naval Research
- Stanford University
- United States Department of Defense
- Özyeğin University