Grid Sample Based Temporal Iteration and Compactness-coefficient Distance for High Frame and Ultra-low Delay SLIC Segmentation System

Abstract

High frame rate and ultra-low delay vision systems, which process 1000 FPS videos within 1 ms/frame delay, play an increasingly important role in fields such as robotics and factory automation. Among them, an image segmentation system is necessary as segmentation is a crucial pre-processing step for various applications. Recently many existing researches focus on superpixel segmentation, but few of them attempt to reach high processing speed. To achieve this target, this paper proposes: (A) Grid sample based temporal iteration, which leverages the high frame rate video property to distribute iterations into the temporal domain, ensuring the entire system is within one frame delay. Additionally, grid sample is proposed to add initialization information to temporal iteration for the stability of superpixels. (B) Compactness-coefficient distance is proposed to add information of the entire superpixel instead of only using the information of the center point. The evaluation results demonstrate that the proposed superpixel segmentation system achieves boundary recall and under-segmentation error comparable to the original SLIC superpixel segmentation system. For label consistency, the proposed system is more than 0.02 higher than the original system.