摘要
粮仓是保障粮食储藏安全的重要设施。粮仓为封闭大空间,仓内光照昏暗、空气流通差,熏蒸、气调等作业增加了人员安全隐患,通过仓内安防视频对作业人员的异常行为进行识别与分析,是作业人员安全操作的一项重要技术保障。提出了一种基于骨架序列多算法的粮仓内作业人员异常行为的视频识别算法。首先,利用YOLOv3tiny模型对人体进行快速检测,结合Sort对多目标进行运动轨迹跟踪,通过AlphaPose模型提取人体骨架坐标序列及权重信息;进而,根据人体骨架自然连接节点构成的实际空间图(RSG)和虚拟人体的重心与头、手、脚互连构建的虚拟空间图(VSG),基于人体动力学重心与手脚互动的平衡性,提取仓内作业人员异常行为的空间特征和串联时间卷积(TC)的时空特征;最后,提出了虚实结合的时空图卷积网络(VR-STGCN)仓内作业人员的异常行为视频识别算法。同时自建了混合数据集,并将VR-STGCN与SSD、PCANet、Two-StreamCNN、STGCN等四种算法进行了对比实验与分析。结果表明:VR-STGCN各项指标均优于其他四种算法;VR-STGCN能够在光线不足、多目标、远距离等复杂环境下准确地识别出仓内人员的跌倒、爬行、躺平等异常行为,识别准确率达到97.7%,处理速度为18.67 fps,能够实时分析作业人员异常行为。研究成果为复杂环境下粮仓作业人员的安全保障提供了一种全新高效的技术。
Granary is an important facility to ensure the safety of grain storage.The granary is a large closed space with dim lighting and poor air circulation.Operations such as fumigation and air conditioning increase personnel safety risks.The identification and analysis of abnormal behaviors of workers through security videos in the granary has become a key safe operations for workers as an important technical guarantee.This paper proposed a video recognition algorithm for abnormal behavior of workers in a granary based on a skeleton sequence multi-algorithm.First,the YOLOv3tiny model was used to quickly detect the human body,combined with Sort to track the motion trajectories of multiple targets,and the human skeleton coordinate sequence and weight information were extracted through the AlphaPose model.Then,based on the real spacial graph(RSG)composed of natural connection nodes of the human skeleton and virtual spacial graph(VSG)constructed by interconnecting the center of gravity of the virtual human body with the head,hands,and feet,the bin was extracted based on the balance of the interaction between the center of gravity of the human body dynamics and the hands and feet.Spatial characteristics of abnormal behavior of internal workers and spatiotemporal characteristics of concatenated temporal convolution(TC).Finally,a virtual-real combining spatial temporal graph convolution network(VR-STGCN)video recognition algorithm for abnormal behavior of granary workers was proposed.At the same time,a hybrid dataset was built,and comparative experiments and analysis were conducted between VR-STGCN and four algorithms such as SSD,PCANet,Two-StreamCNN,and STGCN.The results showed that all indicators of VR-STGCN were better than those of the other four algorithms.VR-STGCN can accurately identify abnormal behaviors such as falling,crawling,and lying down of people in the granary in complex environments such as insufficient light,multiple targets,and long distances.The recognition accuracy reached 97.7%,and the processing speed
作者
侯晓龙
杨卫东
李磊
于俊伟
许启铿
HOU Xiao-ong;YANG Wei-dong;LI Lei;YU Jun-wei;XU Qi-keng(College of Information Science and Engineering,Henan University of Technology,Zhengzhou,Henan 450001,China;School of Artificial intelligence and Big Data,Henan University of Technology,Zhengzhou,Henan 450001,China;College of civil engineering and architecture,Henan University of Technology,Zhengzhou,Henan 450001,China)
出处
《粮油食品科技》
CAS
CSCD
北大核心
2024年第3期201-210,共10页
Science and Technology of Cereals,Oils and Foods
基金
河南省重大公益专项(201300210100)
河南省杰出青年基金(222300420004)
2021年度河南省重点研发与推广专项(212102210152)。
关键词
时空图卷积
异常行为识别
人体动力学
粮仓作业安全
spatial-temporal graph convolution
identification of abnormal behavior
action estimation
grain storehouse operation safety
