Optical flow computation has been extensively used for motion estimation of objects in image sequences. The results obtained by most optical flow techniques are computationally intensive due to the large amount of data involved. A new change-based data flow pipelined architecture has been developed implementing the Horn and Schunk smoothness constraint; pixels of the image sequence that significantly change, fire the execution of the operations related to the image processing algorithm. This strategy reduces the data and, combined with the custom hardware implemented, it achieves a significant optical flow computation speed-up with no loss of accuracy. This paper presents the bases of the change-driven data flow image processing strategy, as well as the implementation of custom hardware developed using an Altera Stratix PCI development board.