基于Faster R-CNN和IoU优化的实验室人数统计与管理系统
|
摘要
针对人员位置相对固定的场景中实时人数统计的管理需求,以普通高校实验室为例,设计并实现了一套基于更快速的区域卷积神经网络(Faster R-CNN)和交并比(IoU)优化的实验室人数统计与管理系统。首先,使用Faster R-CNN模型对实验室内人员头部进行检测;然后,根据模型检测的输出结果,利用IoU算法滤去重复检测的目标;最后,采用基于坐标定位的方法确定实验室内各个工作台是否有人,并将相对应的数据存入数据库。该系统主要功能有:①实验室实时视频监控及远程管理;②定时自动拍照检测采集数据,为实验室的量化管理提供数据支撑;③实验室人员变化数据查询与可视化展示。实验结果表明,所提基于Faster R-CNN和IoU优化的实验室人数统计与管理系统可用于办公场景中实时人数统计和远程管理。
Aiming at the management requirement of real-time personnel statistics in office scenes with relatively fixed personnel positions, a laboratory personnel statistics and management system based on Faster Region-based Convolutional Neural Network(Faster R-CNN) and Intersection over Union(IoU) optimization was designed and implemented with an ordinary university laboratory as the example. Firstly, Faster R-CNN model was used to detect the heads of the people in the laboratory. Then, according to the output results of the model detection, the repeatedly detected targets were filtered by using IoU algorithm. Finally, a coordinate-based method was used to determine whether there were people at each workbench in the laboratory and store the corresponding data in the database. The main functions of the system are as follows: ① real-time video surveillance and remote management of the laboratory; ② timed automatic photo, detection and acquisition of data to provide data support for the quantitative management of the laboratory; ③ laboratory personnel change data query and visualization. The experimental results show that the proposed laboratory personnel statistics and management system based on Faster R-CNN and IoU optimization can be used for real-time personnel statistics and remote management in office scenes.
Aiming at the management requirement of real-time personnel statistics in office scenes with relatively fixed personnel positions, a laboratory personnel statistics and management system based on Faster Region-based Convolutional Neural Network(Faster R-CNN) and Intersection over Union(IoU) optimization was designed and implemented with an ordinary university laboratory as the example. Firstly, Faster R-CNN model was used to detect the heads of the people in the laboratory. Then, according to the output results of the model detection, the repeatedly detected targets were filtered by using IoU algorithm. Finally, a coordinate-based method was used to determine whether there were people at each workbench in the laboratory and store the corresponding data in the database. The main functions of the system are as follows: ① real-time video surveillance and remote management of the laboratory; ② timed automatic photo, detection and acquisition of data to provide data support for the quantitative management of the laboratory; ③ laboratory personnel change data query and visualization. The experimental results show that the proposed laboratory personnel statistics and management system based on Faster R-CNN and IoU optimization can be used for real-time personnel statistics and remote management in office scenes.
引文
[1]何扬名,杜建强,肖贤波.基于区域深度特征的人头检测方法[J].微电子学与计算机,2013,30(11):39-42.(HE Y M,DU J Q,XIAO XB.Detecting human head by depth characteristics of regions[J].Microelectronics&Computer,2013,30(11):39-42.)
[2]夏菁菁,高琳,范勇,等.基于骨架特征的人数统计[J].计算机应用,2014,34(2):585-588.(XIA J J,GAO L,FAN Y,et al.People counting based on skeleton feature[J].Journal of Computer Applications,2014,34(2):585-588.)
[3]叶锋,洪斯婷,郑德城,等.基于Adaboost与背景差分级联的室内人数统计方法[J].福建师范大学学报(自然科学版),2017,33(1):7-13.(YE F,HONG S T,ZHENG D C,et al.A people counting method based on Adaboost and background subtraction in indoor environment[J].Journal of Fujian Normal University(Natural Science Edition),2017,33(1):7-13.)
[4]张晓琪,宋钢.基于多特征协同的人头检测新方法[J].西南师范大学学报(自然科学版),2018,43(7):46-52.(ZHANG XQ,SONG G.A new head detection method oriented for vertical monocular camera way[J].Journal of Southwest China Normal University(Natural Science Edition),2018,43(7):46-52.)
[5]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2014:580-587.
[6]GIRSHICK R.Fast R-CNN[C]//Proceedings of the 2015 IEEEInternational Conference on Computer Vision.Washington,DC:IEEE Computer Society,2015:1440-1448.
[7]REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[C]//NIPS 2015:Proceedings of the 28th International Conference on Neural Information Processing Systems.Cambridge,MA:MITPress,2015:91-99.
[8]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//Proceedings of the2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2016:779-788.
[9]LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]//Proceedings of the 2016 European Conference on Computer Vision,LNCS 9905.Cham:Springer,2016:21-37.
[10]王黎,陆慧娟,叶敏超,等.Faster R-CNN的癌症影像检测方法[J].中国计量大学学报,2018,29(2):136-141.(WANG L,LU H J,YE M C,et al.A cancer image detection method based on Faster R-CNN[J].Journal of China University of Metrology,2018,29(2):136-141.)
[11]HUANG W Q,HUANG M Z,ZHANG Y T.Detection of traffic signs based on combination of GAN and faster R-CNN[J].Journal of Physics:Conference Series,2018,1069(1):012159.
[12]戴陈卡,李毅.基于Faster R-CNN以及多部件结合的机场场面静态飞机检测[J].计算机应用,2017,37(z2):85-88.(DAI CK,LI Y.Aeroplane detection in static aerodrome based on faster R-CNN and multi-part model[J].Journal of Computer Applications,2017,37(z2):85-88.)
[13]胡炎,单子力,高峰.基于Faster R-CNN和多分辨率SAR的海上舰船目标检测[J].无线电工程,2018,48(2):96-100.(HUY,SHAN Z L,GAO F.Ship detection based on Faster R-CNNand multiresolution SAR[J].Radio Engineering,2018,48(2):96-100.)
[14]UIJLINGS J R R,van de SANDE K E A,GEVERS T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171.
[15]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Image Net classification with deep convolutional neural networks[C]//NIPS2012:Proceedings of the 25th International Conference on Neural Information Processing Systems.North Miami Beach,FL:Curran Associates Inc.,2012:1097-1105.
[16]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].ar Xiv Preprint,2014,2014:ar Xiv.1409.1556.
[17]SZEGEDY C,LIU W,JIA Y Q,et al.Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2015:1-9.
[18]HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2016:770-778.
[19]彭刚,杨诗琪,黄心汉,等.改进的基于区域卷积神经网络的微操作系统目标检测方法[J].模式识别与人工智能,2018,31(2):142-149.(PENG G,YANG S Q,HUANG X H,et al.Improved object detection method of micro-operating system based on region convolutional neural network[J].Pattern Recognition and Artificial Intelligence,2018,31(2):142-149.)
[20]LIENHART R,MAYDT J.An extended set of Haar-like features for rapid object detection[C]//Proceedings of the 2002 International Conference on Image Processing.Piscataway,NJ:IEEE,2002:900-903.
[21]NEUBECK A,van GOOL L.Efficient non-maximum suppression[C]//ICPR 2006:Proceedings of the 18th International Conference on Pattern Recognition.Washington,DC:IEEE Computer Society,2006:850-855.
[22]李嘉璇.Tensor Flow技术解析与实战[M].北京:人民邮电出版社,2017:12-16.(LI J X.Tensor Flow Technology Analysis and Practice[M].Beijing:Posts and Telecom Press,2017:12-16.)
[23]EVERINGHAM M,van GOOL L,WILLIAMS C K I,et al.The pascal Visual Object Classes(VOC)challenge[J].International Journal of Computer Vision,2010,88(2):303-338.
[24]刘长龙.Python高效开发实战:Django、Tornado、Flask、Twisted[M].北京:电子工业出版社,2016:175-177.(LIU C L.Efficient Python Development Practices:Django,Tornado,Flask,Twisted[M].Beijing:Publishing House of Electronics Industry,2016:175-177.)
[2]夏菁菁,高琳,范勇,等.基于骨架特征的人数统计[J].计算机应用,2014,34(2):585-588.(XIA J J,GAO L,FAN Y,et al.People counting based on skeleton feature[J].Journal of Computer Applications,2014,34(2):585-588.)
[3]叶锋,洪斯婷,郑德城,等.基于Adaboost与背景差分级联的室内人数统计方法[J].福建师范大学学报(自然科学版),2017,33(1):7-13.(YE F,HONG S T,ZHENG D C,et al.A people counting method based on Adaboost and background subtraction in indoor environment[J].Journal of Fujian Normal University(Natural Science Edition),2017,33(1):7-13.)
[4]张晓琪,宋钢.基于多特征协同的人头检测新方法[J].西南师范大学学报(自然科学版),2018,43(7):46-52.(ZHANG XQ,SONG G.A new head detection method oriented for vertical monocular camera way[J].Journal of Southwest China Normal University(Natural Science Edition),2018,43(7):46-52.)
[5]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2014:580-587.
[6]GIRSHICK R.Fast R-CNN[C]//Proceedings of the 2015 IEEEInternational Conference on Computer Vision.Washington,DC:IEEE Computer Society,2015:1440-1448.
[7]REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[C]//NIPS 2015:Proceedings of the 28th International Conference on Neural Information Processing Systems.Cambridge,MA:MITPress,2015:91-99.
[8]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//Proceedings of the2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2016:779-788.
[9]LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]//Proceedings of the 2016 European Conference on Computer Vision,LNCS 9905.Cham:Springer,2016:21-37.
[10]王黎,陆慧娟,叶敏超,等.Faster R-CNN的癌症影像检测方法[J].中国计量大学学报,2018,29(2):136-141.(WANG L,LU H J,YE M C,et al.A cancer image detection method based on Faster R-CNN[J].Journal of China University of Metrology,2018,29(2):136-141.)
[11]HUANG W Q,HUANG M Z,ZHANG Y T.Detection of traffic signs based on combination of GAN and faster R-CNN[J].Journal of Physics:Conference Series,2018,1069(1):012159.
[12]戴陈卡,李毅.基于Faster R-CNN以及多部件结合的机场场面静态飞机检测[J].计算机应用,2017,37(z2):85-88.(DAI CK,LI Y.Aeroplane detection in static aerodrome based on faster R-CNN and multi-part model[J].Journal of Computer Applications,2017,37(z2):85-88.)
[13]胡炎,单子力,高峰.基于Faster R-CNN和多分辨率SAR的海上舰船目标检测[J].无线电工程,2018,48(2):96-100.(HUY,SHAN Z L,GAO F.Ship detection based on Faster R-CNNand multiresolution SAR[J].Radio Engineering,2018,48(2):96-100.)
[14]UIJLINGS J R R,van de SANDE K E A,GEVERS T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171.
[15]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Image Net classification with deep convolutional neural networks[C]//NIPS2012:Proceedings of the 25th International Conference on Neural Information Processing Systems.North Miami Beach,FL:Curran Associates Inc.,2012:1097-1105.
[16]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].ar Xiv Preprint,2014,2014:ar Xiv.1409.1556.
[17]SZEGEDY C,LIU W,JIA Y Q,et al.Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2015:1-9.
[18]HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2016:770-778.
[19]彭刚,杨诗琪,黄心汉,等.改进的基于区域卷积神经网络的微操作系统目标检测方法[J].模式识别与人工智能,2018,31(2):142-149.(PENG G,YANG S Q,HUANG X H,et al.Improved object detection method of micro-operating system based on region convolutional neural network[J].Pattern Recognition and Artificial Intelligence,2018,31(2):142-149.)
[20]LIENHART R,MAYDT J.An extended set of Haar-like features for rapid object detection[C]//Proceedings of the 2002 International Conference on Image Processing.Piscataway,NJ:IEEE,2002:900-903.
[21]NEUBECK A,van GOOL L.Efficient non-maximum suppression[C]//ICPR 2006:Proceedings of the 18th International Conference on Pattern Recognition.Washington,DC:IEEE Computer Society,2006:850-855.
[22]李嘉璇.Tensor Flow技术解析与实战[M].北京:人民邮电出版社,2017:12-16.(LI J X.Tensor Flow Technology Analysis and Practice[M].Beijing:Posts and Telecom Press,2017:12-16.)
[23]EVERINGHAM M,van GOOL L,WILLIAMS C K I,et al.The pascal Visual Object Classes(VOC)challenge[J].International Journal of Computer Vision,2010,88(2):303-338.
[24]刘长龙.Python高效开发实战:Django、Tornado、Flask、Twisted[M].北京:电子工业出版社,2016:175-177.(LIU C L.Efficient Python Development Practices:Django,Tornado,Flask,Twisted[M].Beijing:Publishing House of Electronics Industry,2016:175-177.)