A Novel Hardware Architecture of the Lucas–Kanade Optical Flow for Reduced Frame Memory Access

摘要

The Lucas–Kanade (LK) algorithm is a cost-efficient gradient-based algorithm for real-time optical flow generation. An excessive external memory access limits the LK algorithm from being broadly used in practical high-frame-rate applications. To overcome this limitation, this paper proposes a novel hardware architecture that stores the input image after the Gaussian filtering operation instead of the original input image itself. The Gaussian-filtered image is downsampled in both the horizontal and vertical directions, thus reducing the external memory access to one quarter of the original data. The downsampling operation does not cause a significant degradation of accuracy because the Gaussian filter is a low-pass filter that reduces the aliasing effect of downsampling. The downsampled pixels are selected in an interleaved manner across multiple frames to reduce the degradation of accuracy. Experimental results show that the proposed algorithm reduces the frame memory access by 61%–75% compared with the previous research.