Object tracking based on the orientation tensor conceptJ

We apply the 3D-orientation tensor representation to construct an object tracking algorithm. 2D-line normal ow is estimated by computing the eigenvector associated with the largest eigenvalue of 3D (two spatial dimensions plus time) tensors with a planar structure. Object's true 2D velocity is computed by averaging tensors with consistent normal ows, generating a 3D line represention that corresponds to a 2D point in motion. Flow induced by camera rotation is compensated for by ignoring points with velocity consistent with the ego-rotation. A region-of-interest growing process based on motion consistency generates estimates of object size and position.