A Transformer-based Multimodal Fusion Model for Efficient Crowd Counting Using Visual and Wireless Signals
This addresses crowd counting for surveillance or public safety applications, but it is incremental as it combines existing methods (Transformers and CNNs) for multimodal fusion.
The paper tackles the problem of information loss in single-modal crowd counting by proposing TransFusion, a model that fuses visual and wireless signals using Transformers and CNNs, achieving high accuracy with minimal errors.
Current crowd-counting models often rely on single-modal inputs, such as visual images or wireless signal data, which can result in significant information loss and suboptimal recognition performance. To address these shortcomings, we propose TransFusion, a novel multimodal fusion-based crowd-counting model that integrates Channel State Information (CSI) with image data. By leveraging the powerful capabilities of Transformer networks, TransFusion effectively combines these two distinct data modalities, enabling the capture of comprehensive global contextual information that is critical for accurate crowd estimation. However, while transformers are well capable of capturing global features, they potentially fail to identify finer-grained, local details essential for precise crowd counting. To mitigate this, we incorporate Convolutional Neural Networks (CNNs) into the model architecture, enhancing its ability to extract detailed local features that complement the global context provided by the Transformer. Extensive experimental evaluations demonstrate that TransFusion achieves high accuracy with minimal counting errors while maintaining superior efficiency.