Abstract
Event cameras offer high temporal resolution and power efficiency, making them well-suited for edge AI applications. However, their high event rates present challenges for data transmission and processing. Subsampling methods provide a practical solution, but their effect on downstream visual tasks remains underexplored. In this work, we systematically evaluate six hardware-friendly subsampling methods using convolutional neural networks for event video classification on various benchmark datasets. We hypothesize that events from high-density regions carry more task-relevant information and are therefore better suited for subsampling. To test this, we introduce a simple causal density-based subsampling method, demonstrating improved classification accuracy in sparse regimes. Our analysis further highlights key factors affecting subsampling performance, including sensitivity to hyperparameters and failure cases in scenarios with large event count variance. These findings provide insights for utilization of hardware-efficient subsampling strategies that balance data efficiency and task accuracy. The code for this paper will be released at: this https URL.
Abstract (translated)
事件相机提供高时间分辨率和电源效率，非常适合边缘AI应用。然而，它们的高事件率给数据传输和处理带来了挑战。降采样方法提供了实用的解决方案，但其对下游视觉任务的影响仍需进一步探索。在这项工作中，我们系统地评估了六种硬件友好的降采样方法，并使用卷积神经网络在各种基准数据集上进行事件视频分类。我们假设来自高密度区域的事件携带更多与任务相关的信息，因此更适合进行降采样。为此，我们引入了一种简单的基于因果密度的降采样方法，在稀疏场景中展示了改进的分类准确性。我们的分析进一步突出了影响降采样性能的关键因素，包括对超参数的敏感性和在具有较大事件计数变化率情况下失效的情况。这些发现为如何利用硬件高效的降采样策略提供了见解，从而平衡数据效率和任务准确性。 本文的代码将在以下网址发布：[此链接](https://this https URL)。
URL
https://arxiv.org/abs/2505.21187
