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An optimisation of Gaussian mixture models for integer processing units

机译：整数处理单元的高斯混合模型的优化

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This paper investigates sub-integer implementations of the adaptive Gaussian mixture model (GMM) for background/foreground segmentation to allow the deployment of the method on low cost/low power processors that lack Floating Point Unit. We propose two novel integer computer arithmetic techniques to update Gaussian parameters. Specifically, the mean value and the variance of each Gaussian are updated by a redefined and generalized "round" operation that emulates the original updating rules for a large set of learning rates. Weights are represented by counters that are updated following stochastic rules to allow a wider range of learning rates and the weight trend is approximated by a line or a staircase. We demonstrate that the memory footprint and computational cost of GMM are significantly reduced, without significantly affecting the performance of background/foreground segmentation.
机译：本文研究了用于背景/前景分割的自适应高斯混合模型（GMM）的子整数实现，以允许将该方法部署在缺少浮点单元的低成本/低功耗处理器上。我们提出了两种新颖的整数计算机算法技术来更新高斯参数。具体来说，每个高斯的均值和方差都通过重新定义的通用“舍入”运算进行更新，该运算模拟了针对大量学习率的原始更新规则。权重由按照随机规则更新的计数器表示，以允许更大范围的学习率，并且权重趋势由直线或阶梯近似。我们证明GMM的内存占用量和计算成本显着降低，而不会显着影响背景/前景分割的性能。

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