用视觉和无线信号融合提升人群计数精度与效率
A Transformer-based Multimodal Fusion Model for Efficient Crowd Counting Using Visual and Wireless Signals
- 结合图像与信道状态信息，用Transformer融合多模态数据
- 在真实数据集上误差低至1.2%，计数准确率显著提升
- 适合需要高精度人群监测的智能安防与公共管理场景
当前人群计数模型多依赖单一模态输入（如图像或无线信号数据），易造成信息丢失且识别性能不佳。为此，我们提出TransFusion，一种基于多模态融合的人群计数模型，整合信道状态信息（CSI）与图像数据。通过利用Transformer网络的强大能力，该模型有效融合两种不同模态的数据，捕获对准确人群估计至关重要的全局上下文信息。然而，尽管Transformer擅长捕捉全局特征，却可能忽略对精确计数至关重要的细粒度局部细节。为此，我们在模型架构中引入卷积神经网络（CNN），增强其提取详细局部特征的能力，以补充Transformer提供的全局上下文。大量实验评估表明，TransFusion在保持卓越效率的同时，实现了高精度计数，计数误差极小。
原文摘要 · Abstract (English)
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.
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