孙龙培,张星,李清泉,刘涛,方志祥

• 1:深圳大学建筑与城市规划学院空间信息智能感知与服务深圳市重点实验室
• 2:河南财经政法大学资源与环境学院
• 3:武汉大学测绘遥感信息工程国家重点实验室

摘要(Abstract)：

针对目前视觉的行人检测技术大多侧重行人识别与分类,而较少考虑行人的空间精确定位的问题,该文提出一种室内多行人目标连续定位方法。该方法基于单目视觉检测方法,构建像素坐标系到世界坐标系的坐标转换模型,并结合卡尔曼滤波和匈牙利算法实现对多行人目标的连续定位与跟踪。实验结果表明,该方法能对多行人目标实现准确区分,定位精度达到亚米级,平均每帧处理时间为53ms,满足定位应用的实时性要求。

关键词(KeyWords)： 行人定位;多目标定位;单目视觉

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目（41801376,41301511,41771473）;; 国家重点研发项目（2016YFB0502203）;; 广东省自然科学基金项目（2018A030313289）;; 深圳市科技创新委员会基础研究项目（JCYJ20170818144544900,JCYJ20170412105839839）

作者(Author): 孙龙培,张星,李清泉,刘涛,方志祥

DOI: 10.16251/j.cnki.1009-2307.2019.12.014

参考文献(References)：

• [1] SERMANET P,KAVUKCUOGLU K,CHINTALA S,et al.Pedestrian detection with unsupervised multi-stage feature learning[C]∥Conference on Computer Vision and Pattern Recognition.Portland:IEEE,2013:3626-3633.
• [2] ZHANG S S,BENENSON R,SCHIELE B.Filtered channel features for pedestrian detection[EB/OL].(2015-05-23)[2019-01-30].Recognitionhttps：∥arxiv.org/abs/1501.05759v1.
• [3] ZHANG L L,LIN L,LIANG X D,et al.Is faster RCNN doing well for pedestrian detection?[C]∥European Conference on Computer Vision.Berlin:Springer,2016:443-457.
• [4] DU X Z,EL-KHAMY M,LEE J,et al.Fused DNN:a deep neural network fusion approach to fast and robust pedestrian detection[C]∥Applications of Computer Vision.[S.l.]:IEEE,2017:953-961.
• [5] LI J N,LIANG X M,SHEN S M,et al.Scale-aware fast R-CNN for pedestrian detection[J].IEEE Transactions on Multimedia,2018,20(4):985-996.
• [6] ZHANG S,BENENSON R,OMRAN M,et al.Towards reaching human performance in pedestrian detection[J].IEEE transactions on pattern analysis and machine intelligence,2018,40(4):973-986.
• [7] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE,2014:580-587.
• [8] HE K M,ZHANG X Y,REN S Q,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J]IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.
• [9] GIRSHICK R.Fast r-cnn[C]∥Proceedings of the IEEE International Conference on Computer Vision.[S.l:s.n.],2015:1440-1448.
• [10]REN S Q,HE K M,GIRSHICK R,et al.Faster RCNN:towards real-time object detection with region proposal networks[C]∥Computer Vision and Pattern Recognition.[S.l:s.n.],2015:91-99.
• [11]DAI J F,LI Y M,HE K,et al.R-fcn:object detection via region-based fully convolutional networks[C]∥Computer Vision and Pattern Recognition.[S.l:s.n.],2016:379-387.
• [12]LIN T Y,DOLLR P,GIRSHICK R,et al.Feature pyramid networks for object detection[EB/OL].[2019-01-30].https：∥www.researchgate.net/publication/311573567_Feature_Pyramid_Networks_for_Object_Detection.
• [13]HE K M,GKIOXARI G,DOLLR P,et al.Mask R-CNN[C]∥2017IEEE International Conference on Computer Vision.Venice,Italy:IEEE,2017:2980-2988.
• [14]REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]∥Proceedings of the IEEE conference on computer vision and pattern recognition.[S.l:s.n.],2016:779-788.
• [15]REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]∥Conference on Computer Vision and Pattern Recognition.Honolulu,HI,USA:IEEE,2017.
• [16]REDMON J,FARHADI A.Yolov3:An incremental improvement[EB/OL].(2018-04-08)[2019-01-30].https：∥arxiv.org/abs/1804.02767.
• [17]LIU W,ANGUELOV D,ERHAN D,et al.Ssd:single shot multibox detector[C]∥European Conference on Computer Vision.Cham:Springer,2016:21-37.
• [18]KUHN H W.The Hungarian method for the assignment problem[J/OL].(2009-11-06)[2019-01-30].https：∥link.springer.com/chapter/10.1007%2F978-3-540-68279-0_2.
• [19]KALMAN R E.A new approach to linear filtering and prediction problems[J].Journal of Basic Engineering,1960,82(1):35-45.
• [20]ZHANG Z Y.A flexible new technique for camera calibration[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.