Abstract
Regarding the passive WiFi sensing based crowd analysis, this paper
first theoretically investigates its limitations, and then proposes a
deep learning based scheme targeted for returning fine-grained crowd
states in large surveillance areas. To this end, three key challenges
are coped with: to relieve the influences of the randomness and sparsity
induced by passive WiFi sensing, an attention-based deep convolutional
autoencoder model is designed to recover accurate crowd density maps in
a way similar to image reconstruction; to combat the anonymity caused by
MAC randomization, following the identification of local high-density
crowds (LHDCs) with the density clustering algorithm, i.e. DM-DBSCAN, a
bidirectional convolutional LSTM based model is employed to infer LHDC
speeds; to overcome the absence of passive WiFi sensing datasets for
model training, three semi-synthetic datasets are produced by emulating
passive WiFi sensing with practical pedestrian tracking datasets.
Extensive experiments confirm that, the proposed scheme significantly
outperforms existing WiFi-based methods in terms of crowd density
estimation and provides superior crowd speed estimation. More
importantly, the scheme can also produce consistent crowd states on a
real-world dataset, revealing that it has the ability to support
accurate, visualized and real-time crowd monitoring in large
surveillance areas.