A template tracker is a tracker based on matching a pre-initialised view of an object with the object's view in an image sequence. Using an error function, the intensity difference between the template view and the templated region in the image is measured. This error measure is used as the basis for a template alignment algorithm that will adjust the template's pose to more accurately register the template view with the view of the object in the image. Some significant problems present themselves with this simple tracker. Extraneous, or non-object, pixels within the template boundaries can cause bias in the registration of the template. Partial occlusions of the object's view in the image can also cause serious bias in the template's pose. Beyond simple occlusions there are transits of occlusions across an object. Occlusion transits are significant because over time they can occlude the entire object in an incremental fashion. If initially the template view is not completely known this kind of occlusion can easily cause a total tracking failure for an object. In this thesis three enhancements of the basic template tracker are proposed: Ownership Masks, Cooperative Templates, and Evolving Views. Ownership Masks are aimed at eliminating the extraneous pixels from the template view. Cooperative templates are used to separate the intensity probabilities when more than one template covers a pixel. Building upon both Ownership Masks and Cooperative Templates, Evolving Views update the template views when occlusion transits are a problem. With these enhancements we have been able to increase the accuracy of tracking objects where large portions of a template contain background pixels. Also occlusions and some types of unocclusions can be detected and discriminated. Finally, some failures in the basic tracker due to occlusion transits have been overcome.

A template tracker is a tracker based on matching a pre-initialised view of an object with the object's view in an image sequence. Using an error function, the intensity difference between the template view and the templated region in the image is measured. This error measure is used as the basis for a template alignment algorithm that will adjust the template's pose to more accurately register the template view with the view of the object in the image. Some significant problems present themselves with this simple tracker. Extraneous, or non-object, pixels within the template boundaries can cause bias in the registration of the template. Partial occlusions of the object's view in the image can also cause serious bias in the template's pose. Beyond simple occlusions there are transits of occlusions across an object. Occlusion transits are significant because over time they can occlude the entire object in an incremental fashion. If initially the template view is not completely known this kind of occlusion can easily cause a total tracking failure for an object. In this thesis three enhancements of the basic template tracker are proposed: Ownership Masks, Cooperative Templates, and Evolving Views. Ownership Masks are aimed at eliminating the extraneous pixels from the template view. Cooperative templates are used to separate the intensity probabilities when more than one template covers a pixel. Building upon both Ownership Masks and Cooperative Templates, Evolving Views update the template views when occlusion transits are a problem. With these enhancements we have been able to increase the accuracy of tracking objects where large portions of a template contain background pixels. Also occlusions and some types of unocclusions can be detected and discriminated. Finally, some failures in the basic tracker due to occlusion transits have been overcome.