Abstract
A work-flow which aims at capturing residents’ abnormal activities through the passenger flow of elevator in multi-storey residence buildings is presented in this paper. Firstly, sensors (hall sensor, photoelectric sensor, gyro, accelerometer, barometer, and thermometer) connected with internet are mounted in elevator to collect image and data. Then 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 so-called GraftNet is proposed for fine-grained multi-label recognition task to recognize human attributes (e.g. gender, age, appearance, and occupation). Thirdly, based on the passenger flow data, anomaly detection of unsupervised learning is hierarchically applied to detect abnormal or even illegal activities of the residents. Meanwhile, based on manual reviewed data, Catboost algorithm is implemented for multi-classification task. Experiment shows the work-flow proposed in this paper can detect the anomaly and classify different categories well.
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https://pan.baidu.com/s/10Cty8nJcpbp9B0_oGXjPww, extraction code: llqi.
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Jia, C., Yi, W., Wu, Y., Li, Z., Zhu, S., Wu, L. (2021). Crowd Anomaly Detection Based on Elevator Internet of Things Technology. In: Peñalver, L., Parra, L. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 365. Springer, Cham. https://doi.org/10.1007/978-3-030-71061-3_1
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