基于无监督学习和细粒度多标签识别的电梯客流异常行为捕获
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition
摘要：
本文提出了一个通过多层住宅楼电梯客流捕捉居民异常活动的工作流程。摄像头和传感器（霍尔传感器、光电传感器、陀螺仪、加速度计、气压计和温度计）与互联网连接安装在电梯中，以收集图像和数据。应用实例分割、多标签识别、嵌入和聚类等计算机视觉算法对电梯客流进行概化，即每层电梯有多少人、什么样的人进出。更具体地说，在我们的实现中，我们提出了GraftNet，细粒度多标签识别任务的解决方案，以识别人类属性，例如性别，年龄，外观和职业。然后对客流数据分层应用无监督学习的异常检测，捕捉到居民可能带来安全隐患的异常甚至违法行为，如贩毒、传销聚集、卖淫嫖娼、过度拥挤等。实验证明效果是存在的，捕获的记录将直接报告给我们的客户（物业经理）进一步确认。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
