基于云台摄像机的快速移动人群的检测与跟踪

常运; 杜玉红; 焉台郎; 赵地; 李兴

doi:10.3788/YJYXS20163110.0998

您当前的位置：

首页 >

文章列表页 >

基于云台摄像机的快速移动人群的检测与跟踪

显示与图像 | 更新时间：2020-07-07

- 基于云台摄像机的快速移动人群的检测与跟踪
- Detection and tracking of fast moving camera based on the PTZ camera
- 液晶与显示 2016年31卷第10期页码：998-1005
- 作者机构：
  
  1.天津工业大学机械工程学院, 天津 300387
  2.天津工业大学天津市现代机电装备技术重点实验室, 天津 300387
- 作者简介：
  
  常运(1990-), 男, 河北秦皇岛人, 硕士研究生, 主要从事图像处理、目标检测与跟踪方面的研究。E-mail:691815779@qq.com
  [ "杜玉红(1974-), 女, 黑龙江绥化人, 博士, 副教授, 主要从事机器视觉及图像处理、模式识别方面的研究。E-mail:DYH202@163.com" ]
- 基金信息：
  
  国家重点基础研究发展计划（973预）(2010CB334711);国家自然科学基金项目(51205288);国家级大学生创新创业训练计划资助项目(201510058053);National Key Basic Research and Development Plan (973 pre)(2010CB334711);National Natural Science Foundation of China(51205288);Project Supported by National College Students Innovation and Entrepreneurship Training Program(201510058053)
- DOI：10.3788/YJYXS20163110.0998
  中图分类号： TP391.41
- 收稿日期：2016-06-24，
  
  录用日期：2016-7-15，
  
  纸质出版日期：2016-10
- 稿件说明：
移动端阅览
常运, 杜玉红, 焉台郎, 等. 基于云台摄像机的快速移动人群的检测与跟踪[J]. 液晶与显示, 2016,31(10):998-1005.

Yun CHANG, Yu-hong DU, Tai-lang YAN, et al. Detection and tracking of fast moving camera based on the PTZ camera[J]. Chinese journal of liquid crystals and displays, 2016, 31(10): 998-1005.
常运, 杜玉红, 焉台郎, 等. 基于云台摄像机的快速移动人群的检测与跟踪[J]. 液晶与显示, 2016,31(10):998-1005. DOI： 10.3788/YJYXS20163110.0998.

Yun CHANG, Yu-hong DU, Tai-lang YAN, et al. Detection and tracking of fast moving camera based on the PTZ camera[J]. Chinese journal of liquid crystals and displays, 2016, 31(10): 998-1005. DOI： 10.3788/YJYXS20163110.0998.

摘要

为了实现校园的安全监控，提出了基于云台摄像机的快速移动人群的检测与跟踪算法。介绍了云台摄像机用于校园安全监控的基本构成。为了更好地实现云台摄像头对于移动人群的检测与跟踪，建立了基于现实的精确摄像机模型，提出了摄像机自旋转角度约束的摄像机模型。通过最新的核相关滤波器跟踪算法（KCF）实现对运动着的人群检测与跟踪。运用Matlab仿真实验比较该方法和卡尔曼滤波跟踪算法，选择最优方法和相关滤波器跟踪算法来实现检测与跟踪要求。结果表明：相比较于传统的卡尔曼滤波跟踪算法，KFC算法的跟踪精度优于传统方法，精度多数情况下能达到90%以上，高于卡尔曼滤波跟踪算法的60%，检测与跟踪效果达到要求。

Abstract

In order to realize the campus safety monitoring

we put forward the detection and tracking of fast moving crowd algorithm based on the PTZ camera. Camera for basic construction of campus safety monitoring is introduced. In order to achieve PTZ camera for better detecting and tracking

the mobile population was established based on the reality of precision of the camera model

the camera rotation angle constraint

the camera model. Detection and tracking of moving people are implemented by using the latest kernel correlation filter tracking algorithm (KCF). The Matlab simulation experiment is used to compare the proposed method and Calman filter tracking algorithm

and the optimal method is selected to detect and track the kernel correlation filter tracking algorithm. The results show that the method is feasible

compared with the traditional Kalman filter tracking algorithm

the tracking accuracy of KFC algorithm is superior to the traditional methods. The accuracy in most cases can reach more than 90%

higher than 60% of the Kalman filter tracking algorithm. Testing and track performance meet the requirements.

关键词

Keywords

references

侯志强, 韩崇昭.视觉跟踪技术综述[J].自动化学报, 2006, 32(4):603-617.

HOU Z Q, HAN C Z. A survey of visual tracking[J]. ActaAutomaticaSinica, 2006, 32(4):603-617. (in Chinese)

WELCH G, BISHOP G. An introduction to the kalman filter[R]. Chapel Hill, NC, USA:University of North Carolina at Chapel Hill, 1995.

CEN G H, MATSUHIRA N, HIROKAWA J, et al. An improved particle filter for service robot self-localization[J].SICE Journal of Control, Measurement, and System Integration, 2009, 2(1):56-63.

王田, 刘伟宁, 韩广良, 等.基于改进MeanShift的目标跟踪算法[J].液晶与显示, 2012, 27(3):396-400.

WANG T, LIU W N, HAN G L, et al.Target tracking algorithm based on improved MeanShift[J]. Chinese Journal of Liquid Crystals and Displays, 2012, 27(3):396-400. (in Chinese)

杨亮, 周勇, 刘歆浏.基于PTZ摄像机的运动目标跟踪控制[J].兵工自动化, 2014(3):65-68.

YANG L, ZHOU Y, LIU X L. Moving objecttracking controlbased on PTZ camera[J]. Ordnance Industry Automation, 2014(3):65-68. (in Chinese)

郭巳秋, 许廷发, 王洪庆, 等.改进的粒子群优化目标跟踪方法[J].中国光学, 2014, 7(5):759-767.

GUO S Q, XU T F, WANG H Q, et al. Object tracking method based on improved particle swarm optimization[J]. Chinese Optics, 2014, 7(5):759-767. (in Chinese)

WU Y, LI M J, YANG M H. Online object tracking:a benchmark[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland:IEEE, 2013:2411-2418.

BOLME D, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceedings of 2010 IEEE Conference On Computer Vision And Pattern Recognition (CVPR), San Francisco:IEEE, 2010:2544-2550.

柯洪昌, 孙宏彬.图像序列的显著性目标区域检测方法[J].中国光学, 2015, 8(5):768-774.

KE H C, SUN H B.A saliency target area detection method of image sequence[J]. Chinese Optics, 2015, 8(5):768-774. (in Chinese)

郑红, 刘振强.基于精确模型的云台摄像机自标定[J].机器人, 2013, 35(3):326-331, 338.

ZHENG H, LIU Z Q. Self-calibration of pan-tilt camera based on accurate model[J].Robot, 2013, 35(3):326-331, 338. (in Chinese)

HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):583-596.

CESA-BIANCHI N. Applications of regularized least squares to classification problems[M]//BEN-DAVID S, CASE J, MARUOKAA.Algorithmic Learning Theory[M].Heidelberg:Springer Berlin, 2004, 3244:14-18.

RIFKIN RM, YEO G, POGGIO T. Regularized least-squares classification[M]//Advances in Learning Theory:Methods, Model and Applications. Nato Science Series Ⅲ:Computer and Systems Sciences. Amsterdam:IOS Press, 2003:131-154.

YU L Y, FAN C X, MING Y. A visual tracker based on improved kernel correlation filter[C]//Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, New York:IMCS, 2015:51-54.

MAHALANOBIS A, KUMAR B V K V, CASASENT D. Minimum average correlation energy filters[J]. Applied Optics, 1987, 26(17):3633-3640.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据