Summary: This paper presents a multiresolution approach to visual motion tracking. In the approach, the foveation mechanism of the human visual system is used to model the multiresolution information perception algorithms of a Transputer-based pyramid visual tracking system. The video images of a moving target are transformed into pyramidal data structures, each of those images consists of multiple image layers with different resolutions by a Gaussian pyramid generation algorithm. The tracking of a moving target over an image sequence is accomplished by performing a foveal search that is based on an iterative intensity pattern correlation along the multiple resolution levels of the Gaussian pyramids of two successive images. Analyses are given as to the efficiency and accuracy of our tracking algorithm, showing that the algorithm is over 160 times faster than conventional mono-resolution tracking methods, with the tracking error within one pixel. To demonstrate the superiority of the multiresolution tracking algorithm in the connection to parallel computation, a scheme for mapping the tracking algorithm into a Transputer-based pyramidal parallel computing structure is proposed in the paper. Experimental results demonstrate good performance of the proposed approach.