Tracking objects using image disparities

Abstract A method and results are presented for a system that finds and tracks known polyhedral objects in 3-space, given a sequence of grey-level images. The object is located in the first frame using model-based search. Image features are then tracked into the next frame using optic flow techniques, and their disparities are used to invert the perspective transform. The system can recognise when it is getting lost during the tracking stage. It recaptures its position through another model search that uses the reduced disparity information, and a ‘best guess’ at position, to constrain the size of the search space. To do this the system integrates three established algorithms in a novel way: model matching is done using modified Goad-search1,2; edgelets are tracked between images using a flow algorithm such as that of Barnard and Thomson3; and the perspective transform is inverted using Lowe's formulation of the projection equations4,5.