| 基于Kinect的地铁乘客不安全行为识别方法与实验* |
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| 引用本文： | 卢颖,吕希凡,郭良杰,仇乐,路越茗.基于Kinect的地铁乘客不安全行为识别方法与实验*[J].中国安全生产科学技术,2021,17(12):162-168. |
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| 作者姓名： | 卢颖 吕希凡 郭良杰 仇乐 路越茗 |
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| 作者单位： | （1.武汉科技大学 资源与环境学院，湖北 武汉 430081；2.中国地质大学（武汉） 工程学院，湖北 武汉 430074) |
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| 基金项目： | * 基金项目: 国家自然科学基金项目（51874213）；湖北省自然科学基金青年项目（2018CFB186）；湖北省应急管理厅安全生产专项（KJZX201907011） |
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| 摘 要： | 为解决地铁视频监控技术对乘客不安全行为只记录不识别且较少考虑识别精确度的问题，提出1种基于Kinect传感器的高效识别方法。以Pelvis为向量起点和动作活动高频关节为终点构建识别特征向量；运用余弦定理获得标准动作与测试动作关节的最大角度差序列；以最大角度差为动作特征量建立相似度计算模型，运用动态时间规整算法（DTW）将初始结果转换为动作相似度。以相连关节法为对照组开展对比实验，结果表明:前者在抽烟、挥拳、挥手呼救等行为识别的准确度分别为91.7%，86.9%，89.2%，平均比对照组高4%以上，显著提高了地铁乘客不安全行为的识别率，可为地铁智能安全管控提供理论与技术依据。
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| 关 键 词： | 不安全行为识别 特征矢量 关节角度 动态时间规整算法（DTW） 地铁乘客 |
Kinect-based recognition method and experiments on unsafe behavior of subway passengers |
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| LU Ying,LYU Xifan,GUO Liangjie,QIU Le,LU Yueming.Kinect-based recognition method and experiments on unsafe behavior of subway passengers[J].Journal of Safety Science and Technology,2021,17(12):162-168. |
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| Authors: | LU Ying LYU Xifan GUO Liangjie QIU Le LU Yueming |
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| Affiliation: | (1.School of Resource & Environmental Engineering,Wuhan University of Science and Technology,Wuhan Hubei 430081,China;2.Faculty of Engineering,China University of Geosciences,Wuhan Hubei 430074,China) |
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| Abstract: | In order to solve the problem that the subway video surveillance technology only records but does not recognize the unsafe behavior of passengers and considers the recognition accuracy less,an efficient recognition method based on Kinect sensor was proposed.The characteristic vectors of recognition were constructed by taking Pelvis as the starting point of the vector and the high-frequency joints of the motive action as the end point,and the maximum angle difference sequence between the standard motion and the test motion joint was obtained through the law of cosines.A similarity calculation model with the maximum angle difference as the motion characteristic quantity was established,then the dynamic time warping algorithm (DTW) was used to convert the initial results into the motion similarity,and the comparative experiments were carried out with the connected joint method as the control group.The results showed that the accuracies of the former on behavior recognition such as smoking,punching,and calling for help were 91.7%,86.9%,and 89.2%,respectively,which were on average 4% higher than the control group.It significantly improves the recognition rate of the unsafe behaviors of subway passengers,and can provide theoretical and technical basis for the intelligent safety management and control of subway. |
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| Keywords: | unsafe behavior recognition characteristic vector joint angle dynamic time warping algorithm (DTW) subway passengers |
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