3D Motion tracking by Kalman filtering

摘要

In this paper, a 3D semantic object motion tracking method based on Kalman filtering is proposed. First, we use a specially designed Color Image Segmentation Editor (CISE) to devise shapes that more accurately describe the object to be tracked. GISE is an integration of edge and region detection, which is based on edge-linking, split-and-merge and the energy minimization for active contour detection. An ROI is further semented into single motion bolbas by considering the constancy of the motion parameters in each blob. Over short time intervals, each blob can be tracked separately and, over loger times, the blobs can be allowed to fragment and coalesce into new blobs as the motion evolves. The tracking of each blob is based on a Kalman filter deived from linearization of a constraint equation satisfied by the pinhole model of a camera. The Kalman filter allows the tracker to project the uncertainties associated with a blob center (or with the coordinates of any other features) into the next frame. This projected uncertainty region can then be searched for the pixels belonging to the blbo. Future work includes investigation of the effects of fillumination changes and simultaneous tracking of multiple targets.