提升事件相机采样效率，兼顾准确率与数据量
Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling
- 按事件密度有选择地采样，聚焦高信息区域
- 在稀疏条件下准确率提升，优于传统方法
- 适合边缘AI中低功耗实时视觉任务
事件相机具备高时间分辨率和低功耗特性，适用于边缘AI应用。但其高事件率给传输与处理带来挑战。本文系统评估了六种硬件友好的采样方法，在多个基准数据集上使用卷积神经网络进行事件视频分类。我们假设高密度区域的事件包含更多任务相关信息，更适合采样。为此提出一种简单因果密度采样方法，在稀疏场景下实现分类准确率提升。分析还揭示了超参数敏感性和事件数量方差大时的失败案例。这些发现为平衡数据效率与任务准确性提供了实用指导。代码将公开于：https://github.com/hesamaraghi/event-camera-subsampling-methods。
原文摘要 · Abstract (English)
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: https://github.com/hesamaraghi/event-camera-subsampling-methods.
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