Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers. However, they are sensitive to background activity (BA) events that are unwanted. We propose HashHeat, a hashing-based spatiotemporal BA filter for DVS. It is the first spatiotemporal filter that doesn't scale with the DVS output size and doesn't store the 32-bits timestamps. We not only give the visual denoising effect of the filter but also use two metrics for quantitatively analyzing the filter's global performance and local performance respectively, where we introduce a novel metric for evaluating global performance. The experimental results show that HashHeat achieves similar global performance as baseline filters, but increases the signal to noise ratio by about 1.5x to nearly 5x compared with other baseline filters concerning the local performance. The hardware implementation enables HashHeat to output a labeled event every 10ns and meets the real-time requirement. And it can reduce the storage cost by 128x to 256x compared with baseline filters.

与基于帧的成像器相比，基于神经形态事件的动态视觉传感器 (DVS) 具有更快的采样率和更高的动态范围。但是，它们对不需要的背景活动 (BA) 事件很敏感。我们提出了 HashHeat，一种用于 DVS 的基于散列的时空 BA 过滤器。它是第一个不随 DVS 输出大小缩放且不存储 32 位时间戳的时空滤波器。我们不仅给出了滤波器的视觉去噪效果，而且还使用了两个指标分别定量分析了滤波器的全局性能和局部性能，其中我们引入了一种评估全局性能的新指标。实验结果表明，HashHeat 实现了与基线滤波器相似的全局性能，但信噪比增加了约 1。与有关局部性能的其他基线滤波器相比，提高了 5 倍到近 5 倍。硬件实现使得HashHeat每10ns输出一个标记事件，满足实时性要求。与基线过滤器相比，它可以将存储成本降低 128 倍到 256 倍。