Title: DualFusion: Dual Adaptive Fusion for Multi-View Pedestrian Detection via View Reliability Modeling and Channel Reweighting
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: information fusion, pedestrian detection, adaptive fusion

Abstract: Multi-view pedestrian detection methods project feature maps from multiple cameras onto a unified bird’s-eye view (BEV), enabling spatially aligned cross-view feature fusion. However, existing methods often adopt uniform fusion strategies, ignoring differences in view reliability—such as occlusion severity and projection distortion—and neglecting semantic correlations across feature channels (e.g., contours or foot-level cues). These limitations lead to noisy feature aggregation and suboptimal detection accuracy. To overcome these challenges, we propose a Dual Adaptive Fusion framework (DualFusion) that enhances BEV representations through two targeted modules: the Cross-View Feature Selector (CVFS) and the View-Channel Graph Attention (VCGA). CVFS, inspired by recent advances in Vision Transformers, employs a Transformer encoder to perform dynamic view-aware fusion in the BEV space, enabling spatially reliable feature aggregation across views. VCGA models joint view-channel dependencies using global-local context pooling and a graph-inspired multilayer perceptron, allowing adaptive channel-wise reweighting of the fused features. Extensive experiments on public benchmarks demonstrate that our method consistently outperforms recent multi-view fusion approaches. Ablation studies further confirm the effectiveness of each component. Our code will be released upon acceptance.