  We present a work-flow which aims at capturing residents' abnormal activities
through the passenger flow of elevator in multi-storey residence buildings.
Camera and sensors (hall sensor, photoelectric sensor, gyro, accelerometer,
barometer, and thermometer) with internet connection are mounted in elevator to
collect image and data. Computer vision algorithms such as instance
segmentation, multi-label recognition, embedding and clustering are applied to
generalize passenger flow of elevator, i.e. how many people and what kinds of
people get in and out of the elevator on each floor. More specifically in our
implementation we propose GraftNet, a solution for fine-grained multi-label
recognition task, to recognize human attributes, e.g. gender, age, appearance,
and occupation. Then anomaly detection of unsupervised learning is
hierarchically applied on the passenger flow data to capture abnormal or even
illegal activities of the residents which probably bring safety hazard, e.g.
drug dealing, pyramid sale gathering, prostitution, and over crowded residence.
Experiment shows effects are there, and the captured records will be directly
reported to our customer(property managers) for further confirmation.
