AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition

2025-05-16

AI Summary

Event cameras’ high temporal resolution introduces substantial redundancy and noise, severely corrupting critical spatiotemporal structures and hindering event-based object recognition. To address this, we propose an end-to-end joint denoising and recognition framework. Our method introduces three key innovations: (1) adaptive event segmentation, (2) a multi-factor edge-weighting mechanism, and (3) graph-structure-aware adaptive denoising—collectively enabling effective noise suppression while preserving structural integrity. Technically, the framework integrates normalized density analysis, weighted graph attention, graph convolutional networks, and spatiotemporal graph modeling. Evaluated on four benchmark datasets, our approach achieves recognition accuracies of 83.77%–99.30%, outperforming the best graph-based methods by up to 8.79%. Moreover, it improves denoising performance by 19.57% over prior approaches and surpasses Euclidean-based methods by 6.26%.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Removing noise from event camera data while preserving critical spatiotemporal information
• Enhancing object recognition accuracy in noisy event-based data
• Integrating adaptive graph-based denoising with multifactorial edge-weighting mechanisms

Innovation

• Adaptive event segmentation via normalized density analysis
• Multifactorial edge-weighting mechanism for noise reduction
• Graph-based denoising preserving spatiotemporal structural features