  Accurate indoor crowd counting (ICC) is a key enabler to many smart
home/office applications. In this paper, we propose a Domain-Agnostic and
Sample-Efficient wireless indoor crowd Counting (DASECount) framework that
suffices to attain robust cross-domain detection accuracy given very limited
data samples in new domains. DASECount leverages the wisdom of few-shot
learning (FSL) paradigm consisting of two major stages: source domain meta
training and target domain meta testing. Specifically, in the meta-training
stage, we design and train two separate convolutional neural network (CNN)
modules on the source domain dataset to fully capture the implicit amplitude
and phase features of CSI measurements related to human activities. A
subsequent knowledge distillation procedure is designed to iteratively update
the CNN parameters for better generalization performance. In the meta-testing
stage, we use the partial CNN modules to extract low-dimension features out of
the high-dimension input target domain CSI data. With the obtained
low-dimension CSI features, we can even use very few shots of target domain
data samples (e.g., 5-shot samples) to train a lightweight logistic regression
(LR) classifier, and attain very high cross-domain ICC accuracy. Experiment
results show that the proposed DASECount method achieves over 92.68\%, and on
average 96.37\% detection accuracy in a 0-8 people counting task under various
domain setups, which significantly outperforms the other representative
benchmark methods considered.
