计算机应用 ›› 2019, Vol. 39 ›› Issue (8): 2235-2241.DOI: 10.11772/j.issn.1001-9081.2019010084
收稿日期:
2019-01-15
修回日期:
2019-03-08
出版日期:
2019-08-10
发布日期:
2019-04-15
通讯作者:
陈皓
作者简介:
陈皓(1978-),男,河北安新人,副教授,博士,CCF会员,主要研究方向:进化计算、工程优化;肖利雪(1992-),女,内蒙古赤峰人,硕士研究生,主要研究方向:计算机智能、数据挖掘;李广(1995-),男,陕西铜川人,硕士研究生,主要研究方向:计算机智能、数据挖掘;潘跃凯(1995-),男,山东聊城人,硕士研究生,主要研究方向:计算机智能、数据挖掘;夏雨(1996-),女,陕西咸阳人,硕士研究生,主要研究方向:计算机智能、数据挖掘。
基金资助:
CHEN Hao, XIAO Lixue, LI Guang, PAN Yuekai, XIA Yu
Received:
2019-01-15
Revised:
2019-03-08
Online:
2019-08-10
Published:
2019-04-15
Supported by:
摘要: 针对人体攻击性行为识别问题,提出一种基于人体关节点数据的攻击性行为识别方法。首先,利用OpenPose获得单帧图像中的人体关节点数据,并通过最近邻帧特征加权法和分段多项式回归完成由人体自遮挡和环境因素所导致缺失值的补全;然后,对每个人体定义动态"安全距离"阈值,如果两人真实距离小于阈值,则构建行为特征矢量,其中包括帧间人体重心位移、人体关节旋转角角速度和发生交互时的最小攻击距离等;最后,提出改进的LightGBM算法w-LightGBM,并对攻击性行为进行识别。采用公共数据集UT-interaction对所提出的攻击性行为分类识别方法进行测试实验,准确率达到95.45%。实验结果表明,所提方法能够有效识别各种角度的攻击性行为。
中图分类号:
陈皓, 肖利雪, 李广, 潘跃凯, 夏雨. 基于人体关节点数据的攻击性行为识别[J]. 计算机应用, 2019, 39(8): 2235-2241.
CHEN Hao, XIAO Lixue, LI Guang, PAN Yuekai, XIA Yu. Aggressive behavior recognition based on human joint point data[J]. Journal of Computer Applications, 2019, 39(8): 2235-2241.
[1] SCHWAPPACH D, SENDLHOFER G, HÄSLER L, et al. Speaking up behaviors and safety climate in an Austrian university hospital[J]. International Journal for Quality in Health Care, 2018, 30(9):701-707. [2] YU S, CHEN H, BROWN R A. Hidden Markov model-based fall detection with motion sensor orientation calibration:a case for real-life home monitoring[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 22(6):1847-1853. [3] RABIEE H, MOUSAVI H, NABI M, et al. Detection and localization of crowd behavior using a novel tracklet-based model[J]. International Journal of Machine Learning and Cybernetics, 2017, 9(12), 1999-2010. [4] LU X D, KOGA T. Real-time motion detection for high-assurance aircraft tracking system using downlink aircraft parameters[J]. Simulation Modelling Practice and Theory, 2016, 65:81-92. [5] STONE E E, SKUBIC M. Fall detection in homes of older adults using the Microsoft Kinect[J]. IEEE Journal of Biomedical and Health Informatics, 2015, 19(1):290-301. [6] CHANG X, MA Z, LIN M, et al. Feature interaction augmented sparse learning for fast Kinect motion detection[J]. IEEE Transactions on Image Processing, 2017, 26(8):3911-3920. [7] OUANANE A, SERIR A. New paradigm for recognition of aggressive human behavior based on bag-of-features and skeleton graph[C]//Proceedings of the 8th International Workshop on Systems, Signal Processing and Their Applications. Piscataway, NJ:IEEE, 2013:133-138. [8] SERRANO I, DENIZ O, BUENO G, et al. Spatio-temporal elastic cuboid trajectories for efficient fight recognition using Hough forests[J]. Machine Vision & Applications, 2018, 29(2):207-217. [9] CAO Z, SIMON T, WEI S. et al. Realtime multi-person 2D pose estimation using part affinity fields[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2017:1302-1310. [10] MERAD D, AZIZ K, IGUERNAISSI R, et al. Tracking multiple persons under partial and global occlusions:application to customers' behavior analysis[J]. Pattern Recognition Letters, 2016, 81:11-20. [11] RAZARI-FAR R, CHAKRABARTI S, SAIF M, et al. An integrated imputation-prediction scheme for prognostics of battery data with missing observations[J]. Expert Systems with Applications, 2016, 115:709-723. [12] 史慧.谈谈人际交往中的空间距离[J].河南水利与南水北调,2007(7):73-74. (SHI H, Talking about the spatial distance in interpersonal communication[J]. Henan Water Resources and South-to-North Water Diversion, 2007(7):73-74.) [13] 费章惠.中国大百科全书:力学[M].北京:中国大百科全书出版社,1987:133. (FEI Z H. Encyclopedia of China:Mechanics[M]. Beijing:Encyclopedia of China Publishing House, 1987:133.) [14] MA X, SHA J, WANG D, et al. Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning[J]. Electronic Commerce Research and Applications, 2018, 31:24-39. [15] CHEN W, FU K, ZUO J, et al. Radar emitter classification for large data set based on weighted-XGboost[J]. IET Radar, Sonar & Navigation, 2017, 11(8):1203-1207. [16] SENER F, IKIZLER-CINBIS N. Two-person interaction recognition via spatial multiple instance embedding[J]. Journal of Visual Communication & Image Representation, 2015, 32:63-73. [17] ZHANG B, ROTA P, CONCI N, et al. Human interaction recognition in the wild:analyzing trajectory clustering from multiple-instance-learning perspective[C]//Proceedings of the 2015 IEEE International Conference on Multimedia and Expo. Piscataway, NJ:IEEE, 2015:1-6. [18] KONG Y, LIANG W, DONG Z, et al. Recognising human interaction from videos by a discriminative model[J]. IET Computer Vision, 2014, 8(4):277-286. [19] KONG Y, FU Y. Modeling supporting regions for close human interaction recognition[C]//Proceedings of the 2014 European Conference on Computer Vision, LNCS 8926. Berlin:Springer, 2014:29-44. [20] NGUYEN N, YOSHITAKA A. Human interaction recognition using independent subspace analysis algorithm[C]//Proceedings of the 2014 IEEE International Symposium on Multimedia.Piscataway, NJ:IEEE, 2014:40-46. [21] el houda SLIMANI K N, BENEZETH Y, SOUAMI F. Human interaction recognition based on the co-occurrence of visual words[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, NJ:IEEE, 2014:461-466. [22] 王佩瑶,曹江涛,姬晓飞.基于改进时空兴趣点特征的双人交互行为识别[J].计算机应用,2016, 36(10):2875-2879. (WANG P Y, CAO J T, JI X F. Two-person interactive behavior recognition based on improved spatio-temporal interest points[J]. Journal of Computer Applications, 2016, 36(10):2875-2879.) [23] 姬晓飞,左鑫孟.基于分阶段视觉共生矩阵序列的双人交互识别[J].计算机工程与设计,2017,38(9).2498-2503. (JI X F, ZUO X M. Human interaction recognition based on multi-stag framework and co-occurring visual matrix sequence[J]. Computer Engineering and Design, 2017, 38(9):2498-2503.) |
[1] | 朱槐雨, 李博. 单阶段多框检测器无人机航拍目标识别方法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3234-3241. |
[2] | 刘晓龙, 王士同. 渐进式分离的开放集模糊域自适应算法[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3127-3131. |
[3] | 杜航原 郝思聪 王文剑. 结合图自编码器与聚类的半监督表示学习方法[J]. 计算机应用, 0, (): 0-0. |
[4] | 陈露 张晓霞 于洪. 基于先验知识的非负矩阵半可解释三因子分解算法[J]. 计算机应用, 0, (): 0-0. |
[5] | 韩舒宁 徐敏 董学士 林青 沈凡凡. 混合伊藤算法求解多尺度着色旅行商问题[J]. 计算机应用, 0, (): 0-0. |
[6] | 李晓杰 崔超然 宋广乐 苏雅茜 吴天泽 张春云. 基于时序超图卷积神经网络的股票趋势预测方法[J]. 计算机应用, 0, (): 0-0. |
[7] | 张建 严珂 马祥. 基于神经网络的复杂垃圾信息过滤算法分析[J]. 计算机应用, 0, (): 0-0. |
[8] | 邱云志 汪廷华 戴小路. 双重特征加权模糊支持向量机[J]. 计算机应用, 0, (): 0-0. |
[9] | 李宗正 周恺卿 丁雷 欧云. 基于基因交换的自适应人工鱼群算法[J]. 计算机应用, 0, (): 0-0. |
[10] | 刘清华 廖士中. 基于随机素描方法的在线核回归[J]. 计算机应用, 0, (): 0-0. |
[11] | 张小清 王晨曦 吕彦 林耀进. 基于ReliefF的层次分类在线流特征选择算法[J]. 计算机应用, 0, (): 0-0. |
[12] | 于婉莹 梁美玉 王笑笑 陈徵 曹晓雯. 基于深度注意力网络的课堂教学视频中学生表情识别与智能教学评估[J]. 计算机应用, 0, (): 0-0. |
[13] | 黄勇康 梁美玉 王笑笑 陈徵 曹晓雯. 基于深度时空残差卷积神经网络的课堂教学视频中多人课堂行为识别[J]. 计算机应用, 0, (): 0-0. |
[14] | 康猛 蒙祖强. 基于局部条件区分能力的高效属性约简算法[J]. 计算机应用, 0, (): 0-0. |
[15] | 谢鑫 张贤勇 王旋晔 唐鹏飞. 变精度邻域等价粒邻域决策树构造算法[J]. 计算机应用, 0, (): 0-0. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||