2024 03 v.35 406-414
基于改进SlowFast算法的电梯乘客异常行为识别
基金项目(Foundation):
浙江省市场监督管理局科技计划项目(No.ZC2021B063)
邮箱(Email):
cjy@cjlu.edu.cn;
DOI:
中文作者单位:
中国计量大学质量与标准化学院;宁波市特种设备检验研究院;
摘要(Abstract):
目的:解决轿厢式电梯内乘客异常行为识别算法无法有效利用视频数据中时序特征的问题。方法:提出一种基于残差支路与BiFormer改进的SlowFast网络算法。该网络结构以RGB视频帧和残差帧作为输入,以多支路提取特征信息,融合慢支路、快支路和残差支路的时空特征,增强对乘客异常行为的敏感性,降低背景变化带来的影响。为增强时间维度信息的有效利用,在快支路与残差支路引入BiFormer结构,以学习帧间关联信息,从而提高网络对乘客异常行为识别准确率。结果:为验证网络算法的有效性,以电梯乘客异常行为数据集验证提出的网络结构。与原SlowFast网络进行对比,改进后的网络识别准确率提高了8.46%。结论:结果表明,所提出的网络算法能够充分利用视频帧中时间维度信息,可有效提高电梯乘客异常行为识别准确率,且在电梯内背景与光线变化较大的情况下,仍然具有较好的识别效果。
关键词(KeyWords):
异常行为识别;深度学习;残差支路;自注意力机制
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参考文献
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[2] 朱德慰,李志海,吴镇炜.基于异常行为监测的人机安全协作方法[J].计算机集成制造系统,2022,28(12):3737-3746.ZHU D W,LI Z H,WU Z W.Abnormal behavior monitoring based method for safe human-robot collaboration[J].Computer Integrated Manufacturing Systems,2022,28(12):3737-3746.
[3] MUHAMMAD S S,GHANI A,KHAN I,et al.Image analysis using human body geometry and size proportion science for action classification[J].Applied Sciences,2020,10(16):5453-5465.
[4] HU X,DAI J,HUANG Y,et al.A weakly supervised framework for abnormal behavior detection and localization in crowded scenes[J].Neurocomputing,2020,383:270-281.
[5] HU X,HUANG Y,GAO X,et al.Squirrel-cage local binary pattern and its application in video anomaly detection[J].IEEE Transactions on Information Forensics and Security,2019,14(4):1007-1022.
[6] ZHAO R,WANG Y,JIA P,et al.Abnormal behavior detection based on dynamic pedestrian centroid model:case study on u-turn and fall-down[J].IEEE Transactions on IntelligentTransportation Systems,2023,4(8):8066-8078.
[7] 邓淼磊,高振东,李磊,等.基于深度学习的人体行为识别综述[J].计算机工程与应用,2022,58(13):14-26.DENG M L,GAO Z D,LI L,et al.Overview of human behavior recognition based ondeep learning[J].Computer Engineering and Applications,2022,58(13):14-26.
[8] 徐涛,田崇阳,刘才华.基于深度学习的人群异常行为检测综述[J].计算机科学,2021,48(9):125-134.XU T,TIAN C Y,LIU C H.Deep learning for abnormal crowd behavior detection:A review[J].Computer Science,2021,48(9):125-134.
[9] LIU H C,CHUAH J H,KHAIRUDDIN A S M,et al.Campus abnormal behavior recognition with temporal segment transformers[J].IEEE Access,2023,11:38471-38484.
[10] 张仁路,高丙朋.基于时序时空双流卷积的异常行为识别[J].现代电子技术,2023,46(3):81-87.ZHANG R L,GAO B P.Abnormal behavior recognition based on time-series spatiotemporal two-stream convolution[J].Modern Electronics Technique,2023,46(3):81-87.
[11] HAO Y,TANG Z,ALZAHRANI B,et al.An end-to-end human abnormal behavior recognition framework for crowds with mentally disordered individuals[J].IEEE Journal of Biomedical and Health Informatics,2022,26(8):3618-3625.
[12] LU Y,YU F,REDDY M K K,et al.Few-shot scene-adaptive anomaly detection[C]//Computer Vision-ECCV 2020:16th European Conference.Glasgow,UK:Springer,2020:125-141.
[13] 聂豪,熊昕,郭原东,等.基于深度学习的视频异常行为识别算法[J].现代电子技术,2020,43(24):110-112,116.LIE H,XIONG X,GUO Y D,et al.Video abnormal behavior identifying algorithm based on deep learning[J].Modern Electronics Technique,2020,43(24):110-112,116.
[14] FEICHTENHOFER C,FAN H,MALIK J,et al.Slowfast networks for video recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.Seoul,Korea:IEEE,2019:6202-6211.
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[16] VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.Long Beach,California,USA:Curran Associates Inc,2017:6000-6010.
[17] CARION N,MASSA F,SYNNAEVE G,et al.End-to-end object detection with transformers[C]//European Conference on Computer Vision.Glasgow,UK:Springer,2020:213-229.
[18] WANG Y,XU Z,WANG X,et al.End-to-end video instance segmentation with transformers[C]//Proceedings of the IEEE/CVF Conferenceon Computer Vision and Pattern Recognition.Nashville,TN,USA:IEEE,2021:8741-8750.
[19] ZHU L,WANG X,KE Z,et al.Biformer:Vision transformer with bi-level routing attention[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Vancouver,BC,Canada:IEEE,2023:10323-10333.
[20] TRAN D,BOURDEV L,FERGUS R,et al.Learning spatiotemporal features with 3d convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision.Santiago,Chile:IEEE,2015:4489-4497.
[21] WANG L,XIONG Y,WANG Z,et al.Temporal segment networks:Towards good practices for deep action recognition[C]//European Conference on Computer Vision.Amsterdam,The Netherlands:Springer,2016:20-36.
[22] CARREIRA J,ZISSERMAN A.Quo vadis,action recognition?a new model and the kinetics dataset[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu,HI,USA:IEEE,2017:6299-6308.
[23] FEICHTENHOFER C,PINZ A,ZISSERMAN A.Convolutional two-stream network fusion for video action recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA:IEEE,2016:1933-1941.
[24] QIU Z,YAO T,MEI T.Learning spatio-temporal representation with pseudo-3d residual networks[C]//Proceedings of the IEEE International Conference on Computer Vision.Venice,Italy:IEEE,2017:5533-5541.
基本信息:
DOI:
中图分类号:TP391.41;TU857
引用信息:
[1]王志恒,陈家焱,李俊宁等.基于改进SlowFast算法的电梯乘客异常行为识别[J].中国计量大学学报,2024,35(03):406-414.
基金信息:
浙江省市场监督管理局科技计划项目(No.ZC2021B063)
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