  Visual tracking usually requires an object appearance model that is robust to
changing illumination, pose and other factors encountered in video. In this
paper, we construct an appearance model using the 3D discrete cosine transform
(3D-DCT). The 3D-DCT is based on a set of cosine basis functions, which are
determined by the dimensions of the 3D signal and thus independent of the input
video data. In addition, the 3D-DCT can generate a compact energy spectrum
whose high-frequency coefficients are sparse if the appearance samples are
similar. By discarding these high-frequency coefficients, we simultaneously
obtain a compact 3D-DCT based object representation and a signal
reconstruction-based similarity measure (reflecting the information loss from
signal reconstruction). To efficiently update the object representation, we
propose an incremental 3D-DCT algorithm, which decomposes the 3D-DCT into
successive operations of the 2D discrete cosine transform (2D-DCT) and 1D
discrete cosine transform (1D-DCT) on the input video data.
