Making Every Event Count: Balancing Data Efficiency and Accuracy in Event Camera Subsampling

2025-05-27

AI Summary

Event cameras’ high event rates cause transmission and computational bottlenecks on edge devices, while existing subsampling methods lack systematic evaluation of their impact on downstream tasks. To address this, we propose the first causality-aware, density-driven subsampling method grounded in the hypothesis that regions of high event density are more task-relevant. Our approach introduces a hardware-friendly, density-weighted causal sampling strategy that preserves spatiotemporal event correlations critical for inference. We conduct a comprehensive empirical study across six subsampling methods within a CNN-based event-to-video classification framework on standard benchmarks (N-Cars, DVS Gesture). Results show our method improves classification accuracy by up to 4.2% at low sampling rates. We further identify its sensitivity to hyperparameters and failure boundaries under large event-rate variance. Crucially, we isolate key factors governing subsampling efficacy—providing actionable, deployable design principles for efficient event-camera utilization in resource-constrained edge scenarios.

Technology Category

• Computer Vision: Motion & Tracking
• Reasoning under Uncertainty: Causality
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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: https://github.com/hesamaraghi/event-camera-subsampling-methods.

Problem

• Evaluating subsampling methods for event camera data efficiency
• Exploring impact of subsampling on event video classification accuracy
• Proposing density-based subsampling to improve sparse regime performance

Innovation

• Density-based subsampling improves classification accuracy
• Evaluates six hardware-friendly subsampling methods systematically
• Balances data efficiency and task accuracy effectively