用自适应图网络同时去噪和识别事件相机数据中的物体
AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition
- 基于密度分析自适应分割事件，构建带权重的时空图
- 在四个数据集上准确率最高达99.30%，比现有方法高8.79%
- 适合做事件相机实时感知与智能视觉系统的研究者
事件相机以高时间分辨率捕捉亮度变化，但会产生大量冗余噪声数据，影响物体识别。本文提出一种自适应图结构的噪声去除框架，通过基于归一化密度分析的事件自适应分割、多因子边权重机制及自适应图去噪策略，有效融合时空信息，在保留关键结构特征的同时滤除噪声。在四个挑战性数据集上的实验表明，该方法识别准确率分别达到83.77%、76.79%、99.30%和96.89%，相比现有图方法最高提升8.79%，噪声去除性能提升最高达19.57%，较传统欧氏方法额外提升6.26%。
原文摘要 · Abstract (English)
Event cameras, which capture brightness changes with high temporal resolution, inherently generate a significant amount of redundant and noisy data beyond essential object structures. The primary challenge in event-based object recognition lies in effectively removing this noise without losing critical spatial-temporal information. To address this, we propose an Adaptive Graph-based Noisy Data Removal framework for Event-based Object Recognition. Specifically, our approach integrates adaptive event segmentation based on normalized density analysis, a multifactorial edge-weighting mechanism, and adaptive graph-based denoising strategies. These innovations significantly enhance the integration of spatiotemporal information, effectively filtering noise while preserving critical structural features for robust recognition. Experimental evaluations on four challenging datasets demonstrate that our method achieves superior recognition accuracies of 83.77%, 76.79%, 99.30%, and 96.89%, surpassing existing graph-based methods by up to 8.79%, and improving noise reduction performance by up to 19.57%, with an additional accuracy gain of 6.26% compared to traditional Euclidean-based techniques.
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