Sample-Efficient Cross-Domain WiFi Indoor Crowd Counting via Few-shot Learning

Hou, Huawei; Bi, Suzhi; Zheng, Lili; Lin, Xiaohui; Quan, Zhi

doi:10.1109/wocc55104.2022.9880570

Cited by 4 publications

References 36 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Review of few-shot learning application in CSI human sensing

Wang,

Li,

Wang

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

Wi-Fi sensing has garnered increasing interest for its significant advantages, primarily leveraging Wi-Fi signal fluctuations induced by human activities and advanced neural network algorithms. However, its application faces challenges due to limited generalizability, necessitating frequent data recollection and neural network retraining for adaptation to new environments. To address these limitations, some researchers introduced few-shot learning into Wi-Fi sensing applications because it offers a promising solution with its ability to achieve remarkable performance in novel scenarios using minimal training samples. Despite its potential, a comprehensive review of its applications within this domain remains absent. This study endeavors to fill this gap by exploring prominent Wi-Fi sensing applications that incorporate few-shot learning, aiming to delineate their key features. We categorize few-shot learning approaches into three distinct methodologies: transfer learning, metric learning, and meta-learning, based on their neural network training strategies. Through this classification, we examine representative systems from an application perspective and elucidate the principles of few-shot learning implementation. These systems are evaluated in terms of learning methodology, data modality, and recognition accuracy. Finally, this paper highlights the challenges and future directions for few-shot learning in Channel State Information (CSI) based human sensing, providing a valuable resource for researchers in the field of Wi-Fi human sensing leveraging few-shot learning.

show abstract

Review of few-shot learning application in CSI human sensing

Wang,

Li,

Wang

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

Context-adaptable radar-based people counting via few-shot learning

Mauro¹,

Martínez-Rodríguez²,

Ott³

et al. 2023

Appl Intell

View full text Add to dashboard Cite

In many industrial or healthcare contexts, keeping track of the number of people is essential. Radar systems, with their low overall cost and power consumption, enable privacy-friendly monitoring in many use cases. Yet, radar data are hard to interpret and incompatible with most computer vision strategies. Many current deep learning-based systems achieve high monitoring performance but are strongly context-dependent. In this work, we show how context generalization approaches can let the monitoring system fit unseen radar scenarios without adaptation steps. We collect data via a 60 GHz frequency-modulated continuous wave in three office rooms with up to three people and preprocess them in the frequency domain. Then, using meta learning, specifically the Weighting-Injection Net, we generate relationship scores between the few training datasets and query data. We further present an optimization-based approach coupled with weighting networks that can increase the training stability when only very few training examples are available. Finally, we use pool-based sampling active learning to fine-tune the model in new scenarios, labeling only the most uncertain data. Without adaptation needs, we achieve over 80% and 70% accuracy by testing the meta learning algorithms in new radar positions and a new office, respectively. Graphical abstract

show abstract