WhoFi: Deep Person Re-Identification via Wi-Fi Channel Signal Encoding
This addresses person re-identification for video surveillance by offering a non-visual alternative, though it appears incremental as it builds on existing methods with a new modality.
The paper tackles person re-identification by using Wi-Fi signals instead of visual data to overcome issues like poor lighting and occlusion, achieving competitive results on the NTU-Fi dataset.
Person Re-Identification is a key and challenging task in video surveillance. While traditional methods rely on visual data, issues like poor lighting, occlusion, and suboptimal angles often hinder performance. To address these challenges, we introduce WhoFi, a novel pipeline that utilizes Wi-Fi signals for person re-identification. Biometric features are extracted from Channel State Information (CSI) and processed through a modular Deep Neural Network (DNN) featuring a Transformer-based encoder. The network is trained using an in-batch negative loss function to learn robust and generalizable biometric signatures. Experiments on the NTU-Fi dataset show that our approach achieves competitive results compared to state-of-the-art methods, confirming its effectiveness in identifying individuals via Wi-Fi signals.