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The users’ interactive experience with the computer is enhanced when the computer tracks their position and orientation, referred to as the pose. The distributed multi-sensor tracker will compute the users’ poses without inconveniencing the users with wearing gadgets so that they can interact with the computer for several hours at a time. In virtual environments the computer system immerses the users in the virtual world by displaying the world from their point of view. CAD and computer animators can better visualize their concepts by viewing them in three dimensions rather than in two dimensions on a screen. Auto stereoscopic screens display in 3D based on the user’s head pose. In addition, the users would benefit from the computer system tracking them in their office and displaying useful information, such as calendars and notices, on appropriate screens depending on where the users are in their offices. 

The multi-sensor system tracks the user with many sensors that are positioned throughout the user’s office. Each sensor is connected to a processing unit that can compute parts of several dimensions of the user’s pose. The sensors are interconnected by a network, which communicates the sensor data to where the data is combined into all the dimensions of the user’s pose. Points on the user are detected by sensors, which have a high sampling rate to accurately capture the user’s motion. 

Concepts from SCAAT (Single constraint at a time) [Welch 1997] and directed diffusion [Intanagonwiwat 2000] are used in the multi-sensor tracker. SCAAT provides the mathematical model to compute the user’s pose from every individual sensor reading; however all the computations are processed at a central processor. The multi-sensor tracker will distribute the tracking computation onto the sensor’s processing units. The directed diffusion network provides the communication mechanism between the sensors. Naming the network nodes and the configuration of the network depends on the tracking algorithm.

The effectiveness of the tracker depends on how well it scales, how much human effort it takes to initialize, and its stability in tracking. The scalability of the tracker is evaluated by how many sensors can be added to the system. More sensors enable tracking the user’s pose in a larger volume and increase the tracking accuracy. Each sensor will require bandwidth from the network, and sensors can be added until the network bandwidth limit is reached. A tracking system of many sensors is more appealing if initializing the state of the individual sensors can be automated. A stable tracker needs to continue to accurately track even when there are obstructions to the sensors. 

The multi-sensor tracker is explored in the following 5 sections. Section 2 compares the multi-sensor tracker to other trackers and explains SCAAT and directed diffusion in more detail. The auto stereoscopic display is described in Section 3 to illustrate the accuracy needed by the multi-sensor tracker. The sensor and pose data used by the multi-sensor tracker are described in section 4. Section 5 compares the design choices made for the tracking algorithm.  The conclusion is in Section 6.