Industrial IoT Technologies and Applications. 4th EAI International Conference, Industrial IoT 2020, Virtual Event, December 11, 2020, Proceedings

Research Article

Crowd Anomaly Detection Based on Elevator Internet of Things Technology

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  • @INPROCEEDINGS{10.1007/978-3-030-71061-3_1,
        author={Chunhua Jia and Wenhai Yi and Yu Wu and Zhuang Li and Shuai Zhu and Leilei Wu},
        title={Crowd Anomaly Detection Based on Elevator Internet of Things Technology},
        proceedings={Industrial IoT Technologies and Applications. 4th EAI International Conference, Industrial IoT 2020, Virtual Event, December 11, 2020, Proceedings},
        proceedings_a={INDUSTRIALIOT},
        year={2021},
        month={7},
        keywords={IoT Anomaly Computer Vision Machine learning Big Data Cloud computing},
        doi={10.1007/978-3-030-71061-3_1}
    }
    
  • Chunhua Jia
    Wenhai Yi
    Yu Wu
    Zhuang Li
    Shuai Zhu
    Leilei Wu
    Year: 2021
    Crowd Anomaly Detection Based on Elevator Internet of Things Technology
    INDUSTRIALIOT
    Springer
    DOI: 10.1007/978-3-030-71061-3_1
Chunhua Jia1, Wenhai Yi1, Yu Wu1, Zhuang Li1, Shuai Zhu1, Leilei Wu1
  • 1: Shanghai Elevator Media Information Co., Ltd.

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.