Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition
This work addresses safety hazards like drug dealing or overcrowding for property managers, but it is incremental as it combines existing methods in a new application.
The paper tackles the problem of detecting abnormal activities in multi-storey residence buildings by analyzing elevator passenger flow using sensors and computer vision, proposing GraftNet for fine-grained multi-label recognition of human attributes, and applying unsupervised learning for anomaly detection, with experiments showing effects and captured records reported to property managers.
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.