Low Complexity, Hardware-Efficient Neighbor-Guided SGM Optical Flow for Low-Power Mobile Vision Applications

Abstract

Accurate, low-latency, and energy-efficient optical flow estimation is a fundamental kernel function to enable several real-time vision applications on mobile platforms. This paper presents neighbor-guided semi-global matching (NG-fSGM), a new low-complexity optical flow algorithm tailored for low-power mobile applications. NG-fSGM obtains high accuracy optical flow by aggregating local matching costs over a semi-global region, successfully resolving local ambiguity in texture-less and occluded regions. The proposed NG-fSGM aggressively prunes the search space based on neighboring pixels’ information to significantly lower the algorithm complexity from the original fSGM. As a result, NG-fSGM achieves $17.9{\times}$ reduction in the number of computations and $8.37{\times}$ reduction in memory space compared to the original fSGM without compromising its algorithm accuracy. A multicore architecture for NG-fSGM is implemented in hardware to quantify algorithm complexity and power consumption. The proposed architecture realizes NG-fSGM with overlapping blocks processed in parallel to enhance throughput and to lower power consumption. The eight-core architecture achieves 20 M pixel/s (66 frames/s for VGA) throughput with 9.6 mm2 area at 679.2-mW power consumption in 28-nm node.