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We present a work-flow which aims at capturing residents' abnormal activitiesthrough 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 tocollect image and data. Computer vision algorithms such as instancesegmentation, multi-label recognition, embedding and clustering are applied togeneralize passenger flow of elevator, i.e. how many people and what kinds ofpeople get in and out of the elevator on each floor. More specifically in ourimplementation we propose GraftNet, a solution for fine-grained multi-labelrecognition task, to recognize human attributes, e.g. gender, age, appearance,and occupation. Then anomaly detection of unsupervised learning ishierarchically applied on the passenger flow data to capture abnormal or evenillegal 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 directlyreported to our customer(property managers) for further confirmation.

Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition