刘婧;杨旭;刘董经典;牛强;

• 1:中国矿业大学计算机科学与技术学院
• 2:中国矿业大学教育部矿山数字化工程研究中心

摘要(Abstract)：

吸烟检测已成为公共场所禁烟的重要措施,基于视频图像的吸烟动作识别已广泛用于吸烟检测中。使用深度学习的方法进行图像处理,需要大量数据集训练模型。现有的吸烟动作识别方法的准确率和实时性不够理想,且多只针对一个人进行动作识别。为解决这些问题,提出了一种通过检测周期性动作来识别多人吸烟动作的方法。在进行了大量的实验后发现吸烟行为是有节奏和周期性的,对此具体分析了吸烟行为的周期性并制定了吸烟行为规范;利用人体关节点信息,关注关节点的运动轨迹,检测运动轨迹是否符合周期性规律从而实现吸烟动作识别;同时跟踪多人关节点的信息,以实现多个人实时吸烟行为的识别。实验结果表明,该方法可以达到91%的准确率,在各种情况下都可以保持较高准确率和鲁棒性。

关键词(KeyWords)： 人体关节点;周期性;多人吸烟动作识别

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（51674255）

作者(Author): 刘婧;杨旭;刘董经典;牛强;

Email:

DOI:

参考文献(References)：

• [1] DU Y,WANG W,WANG L.Hierarchical recurrent neural network for skeleton based action recognition[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition,2015.
• [2] WANG L,XIONG Y,WANG Z,et al.Temporal segment networks:Towards good practices for deep action recognition[C]//Proceedings of European Conference on Computer Vision,2016.
• [3] SIMON T,JOO H M.Hand keypoint detection in single images using multiview bootstrapping[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017:1145-1153.
• [4] 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,2017:5533-5541.
• [5] NIE B X,WEI P,ZHU S C.Monocular 3D human pose estimation by predicting depth on joints[C]//Proceedings of 2017 IEEE International Conference on Computer Vision(ICCV),2017:3467-3475.
• [6] TEKIN B.Direct prediction of 3D body poses from motion compensated sequences[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR),2016.
• [7] LI S J,CHAN,ANTONI B.3D human pose estimation from monocular images with deep convolutional neural network[C]//Proceedings of Asian Conference on Computer Vision,2014:332-347.
• [8] TEKIN B,KATIRCIOGLU I.Structured prediction of 3D human pose with deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR),2016.
• [9] PAVLAKOS G,ZHOU X,DERPANIS K G,et al.Coarse-tofine volumetric prediction for single-image 3D human pose[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR),2017.
• [10] 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,2015:4489-4497.
• [11] HOU R,CHEN C,SHAH M.Tube Convolutional Neural Network(T-CNN)for action detection in videos[C]//Proceedings of the IEEE International Conference on Computer Vision,2017:5822-5831.
• [12] SUN L,JIA K,YEUNG D Y,et al.Human action recognition using factorized spatio-temporal convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision,2015:4597-4605.
• [13] VAROL G,LAPTEV I,SCHMID C.Long-term temporal convolutions for action recognition[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2018,40(6):1510-1517.
• [14] MAHAJAN D,GIRSHICK R.Exploring the limits of weakly supervised pretraining[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:181-196.
• [15] YANS J,XIONG Y,LIN D,et al.Spatial temporal graph convolutional networks for skeleton-based action recognition[C]//Proceedings of National Conference on Artificial Intelligence,2018:7444-7452.
• [16] HE K M,GEORGIA G,PIOTR D,et al.Mask R-CNN[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020:42(2):386-397.
• [17] LIANG J W,JIANG L.Peeking into the future:Predicting future person activities and locations in videos[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2019:5725-5734.
• [18] KE Q,BENNAMOUN M,AN S,et al.A new representation of skeleton sequences for 3D action recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017:3288-3297.
• [19] KIM T S,REITER A.Interpretable 3D human action analysis with temporal convolutional,networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops,2017:1623-1631.
• [20] FANG H S,XIE S,TAI Y W,et al.Rmpe:Regional multi-person pose estimation[C]//Proceedings of the IEEE International Conference on Computer Vision,2017:2334-2343.
• [21] SUN K.Deep high-resolution representation learning for human pose estimation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2019:5693-5703.
• [22] CAO Z,SIMON T,WEI S E,et al.Realtime multi-person2D pose estimation using part affinity fields[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017:7291-7299.
• [23] OSOKIN D.Real-time 2D multi-person pose estimation on CPU:Lightweight open pose[J].arXiv:1811.12004,2018.
• [24] WEI S E,Ramakrishna V,Kanade T,et al.Convolutional pose machines[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016:4724-4732
• [25] WOJKE N,BEWLEYA,PAULUS D.Simple online and realtime tracking with a deep association metric[C]//Proceedings of 2017 IEEE International Conference on Image Processing(ICIP),2017:3645-3649.
• [26] KUEHNE H,JHUANG H.HMDB:A large video database for human motion recognition[C]//Proceedings of2011 International Conference on Computer Vision,2011:2556-2563.
• [27] CHIU C F,KUO C H,CHANG P C.Smoking action recognition based on spatial-temporal convolutional neural networks[C]//Proceedings of 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference(APSIPA ASC),2018.