用双金字塔网络提升WiFi信号活动检测精度
WiFi CSI Based Temporal Activity Detection via Dual Pyramid Network
- 分高低频特征学习，用符号掩码注意力聚焦关键区域
- 在2114段活动数据上达到优于基线的检测性能
- 适合智能安防与无感监控场景使用
针对基于WiFi信号的时序活动检测难题，本文提出一种高效的双金字塔网络，融合时序信号语义编码器与局部敏感响应编码器。时序信号语义编码器将特征学习分解为高频与低频分量，采用创新的符号掩码注意力机制突出重要区域，抑制无关部分，并通过ContraNorm进行特征融合。局部敏感响应编码器捕捉信号波动但无需参数学习。两个特征金字塔通过新型交叉注意力机制融合。我们还构建了一个新数据集，包含553个WiFi CSI样本，共2114段活动片段，每段约85秒。大量实验表明，该方法显著优于多个基准模型。
原文摘要 · Abstract (English)
We address the challenge of WiFi-based temporal activity detection and propose an efficient Dual Pyramid Network that integrates Temporal Signal Semantic Encoders and Local Sensitive Response Encoders. The Temporal Signal Semantic Encoder splits feature learning into high and low-frequency components, using a novel Signed Mask-Attention mechanism to emphasize important areas and downplay unimportant ones, with the features fused using ContraNorm. The Local Sensitive Response Encoder captures fluctuations without learning. These feature pyramids are then combined using a new cross-attention fusion mechanism. We also introduce a dataset with over 2,114 activity segments across 553 WiFi CSI samples, each lasting around 85 seconds. Extensive experiments show our method outperforms challenging baselines.
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