Enhancing Egocentric Object Detection in Static Environments using Graph-based Spatial Anomaly Detection and Correction

2025-08-11

AI Summary

To address inconsistent predictions, missed detections, and misclassifications in static first-person object detection—caused by neglecting spatial layout priors—this paper proposes a graph neural network (GNN)-based post-processing framework. It introduces graph-structured modeling for egocentric spatial context for the first time: objects serve as nodes, while geometric and semantic proximity relations define edges, forming a scene graph; GNNs then aggregate neighborhood information to automatically identify and rectify detection anomalies. The method is detector-agnostic, compatible with mainstream detectors such as YOLOv7 and RT-DETR, and requires only minimal human annotation for training. On standard benchmarks, it achieves up to a 4.0% improvement in mAP@50, significantly enhancing detection consistency and robustness under occlusion and cluttered scenes. Key contributions include: (1) the first spatial relational graph modeling paradigm tailored for egocentric vision, and (2) a plug-and-play detection refinement approach that requires no detector retraining.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Object Detection & Categorization
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic 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

In many real-world applications involving static environments, the spatial layout of objects remains consistent across instances. However, state-of-the-art object detection models often fail to leverage this spatial prior, resulting in inconsistent predictions, missed detections, or misclassifications, particularly in cluttered or occluded scenes. In this work, we propose a graph-based post-processing pipeline that explicitly models the spatial relationships between objects to correct detection anomalies in egocentric frames. Using a graph neural network (GNN) trained on manually annotated data, our model identifies invalid object class labels and predicts corrected class labels based on their neighbourhood context. We evaluate our approach both as a standalone anomaly detection and correction framework and as a post-processing module for standard object detectors such as YOLOv7 and RT-DETR. Experiments demonstrate that incorporating this spatial reasoning significantly improves detection performance, with mAP@50 gains of up to 4%. This method highlights the potential of leveraging the environment's spatial structure to improve reliability in object detection systems.

Problem

• Correcting inconsistent object detection in static environments
• Leveraging spatial relationships to fix detection anomalies
• Improving egocentric object detection using graph-based methods

Innovation

• Graph-based spatial anomaly detection and correction
• GNN model for object class validation
• Post-processing module enhancing standard detectors