Detection and tracking method of cross target based on DPM detector and KCF tracker

Yi-han CUI; Tao CHEN; Bao-gang CHEN

doi:10.3788/YJYXS20183312.1026

您当前的位置：

首页 >

文章列表页 >

Detection and tracking method of cross target based on DPM detector and KCF tracker

Image Processing | 更新时间：2020-08-12

- Detection and tracking method of cross target based on DPM detector and KCF tracker
- Chinese Journal of Liquid Crystals and Displays Vol. 33, Issue 12, Pages: 1026-1032(2018)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20183312.1026
  CLC： TP394.1;TH691.9
- Received：25 June 2018，
  
  Accepted：06 September 2018，
  
  Published：2018-12
- 稿件说明：
移动端阅览
Yi-han CUI, Tao CHEN, Bao-gang CHEN. Detection and tracking method of cross target based on DPM detector and KCF tracker[J]. Chinese journal of liquid crystals and displays, 2018, 33(12): 1026-1032.
DOI：

Yi-han CUI, Tao CHEN, Bao-gang CHEN. Detection and tracking method of cross target based on DPM detector and KCF tracker[J]. Chinese journal of liquid crystals and displays, 2018, 33(12): 1026-1032. DOI： 10.3788/YJYXS20183312.1026.

摘要

为了实现对十字靶标的自动检测与跟踪，建立了十字靶标检测跟踪模型。针对目标检测中运算量大、实时性差、目标跟踪需要人工标定视频初始帧的问题，提出了一种基于可变形部件模型（DPM）和核相关滤波器（KCF）的十字靶标检测跟踪算法。首先，提取十字样本集的梯度方向直方图（HOG）特征，采用Latent SVM分类器训练特征集，生成十字靶标物体类的DPM模型。然后，通过滑动窗口搜索匹配方法遍历待检测图片。最后，将检测到的结果作为跟踪算法的起始跟踪帧，应用KCF算法快速跟踪目标。当跟踪目标丢失时，暂时停止跟踪，利用DPM模型重新检测定位目标再进行跟踪。实验结果表明：采用DPM模型检测的平均帧率为1 fps，结合DPM和KCF算法，实时检测跟踪的平均帧率为40 fps。采用基于可变形部件模型（DPM）和核相关滤波器（KCF）的十字靶标检测跟踪算法，实现了目标的自动检测与实时跟踪，且检测速度明显高于传统算法，并且在目标漂移或丢失后自动重新定位并继续跟踪，完成十字靶标的长时间跟踪。

Abstract

An automatic detection and tracking model of cross target is established in order to realize automatic detection and tracking of cross target in this paper. There is a large amount of computation and poor real-time performance in the traditional target detection algorithm. And the target tracking requires manual calibration of the initial frame of video. To solve the problems above

a cross target detection and tracking algorithm is proposed based on Deformable Part Model (DPM) and Kernelized Correlation Filter (KCF). First

the Histogram of Oriented Gradient (HOG) feature of cross sample set is extracted. The feature set is trained by using Latent SVM classifier

and the DPM model of target object class is generated. Then the image to be detected is traversed by sliding window search matching method. Finally

using the detected result as the starting tracking frame of the tracking algorithm

the KCF algorithm is applied to track the target quickly. When the tracking target is lost

the tracking is stopped temporarily. The positioning target is redetected with the DPM model

and then tracked with KCF algorithm. Experimental results indicate that the average frame rate detected by DPM model is 1 fps.The average frame rate of real-time detection and tracking is 40 fps combined with DPM and KCF algorithm. By using the algorithm proposed in this paper

it realizes automatic detection and real-time tracking of the target

and the detection speed is obviously higher than the traditional algorithm. After the target lost

it automatically locks and keeps on tracking

realizing the goal of long tracking of the cross target.

关键词

Keywords

references

SMEULDERS A W M, CHU D M, CUCCHIARA R, et al. Visual tracking:an experimental survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7):1442-1468.

GAVRILA D M, PHILOMIN V. Real-time object detection for "smart" vehicles[C]//Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece: IEEE, 2002: 87-93.

FELZENSZWALB P, MCALLESTER D, RAMANAN D. A discriminatively trained, multiscale, deformable part model[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA: IEEE, 2008: 1-8. https://www.researchgate.net/publication/221363206_A_Discriminatively_Trained_Multiscale_Deformable_Part_Model

BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 2010: 2544-2550. https://www.researchgate.net/publication/221362729_Visual_object_tracking_using_adaptive_correlation_filters

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.

DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005: 886-893. http://www.oalib.com/references/16879692

WILLIAMS C K I. Learning with kernels:support vector machines, regularization, optimization, and beyond[J]. Journal of the American Statistical Association, 2003, 98(462):489.

FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9):1627-1645.

DANELLJAN M, HÄGER G, KHAN F S, et al. Accurate scale estimation for robust visual tracking[C]//Proceedings of the British Machine Vision Conference. Nottingham: BMVA Press, 2014: 65.1-65.11.

LI Y, ZHU J K. A scale adaptive kernel correlation filter tracker with feature integration[C]//Proceedings of the Computer Vision-ECCV 2014 Workshops. Zurich, Switzerland: Springer, 2014: 254-265.

COMANICIU D, RAMESH V, MEER P. Real-time tracking of non-rigid objects using mean shift[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Hilton Head Island, SC, USA: IEEE, 2000: 142-149. https://www.researchgate.net/publication/2355023_Real-Time_Tracking_of_Non-Rigid_Objects_using_Mean_Shift

HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//Proceedings of the 12th European Conference on Computer Vision. Florence, Italy: Springer, 2012: 702-715. https://link.springer.com/chapter/10.1007%2F978-3-642-33765-9_50

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.

CHANG Y, DU Y H, YAN T L, 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. (in Chinese)

HARE S, GOLODETZ S, SAFFARI A, et al. Struck:structured output tracking with kernels[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10):2096-2109.

杨德东, 蔡玉柱, 毛宁, 等.采用核相关滤波器的长期目标跟踪[J].光学精密工程, 2016, 24(8):2037-2049.

YANG DD, CAI Y Z, MAO N, et al. Long-term object tracking based on kernelized correlation filters[J]. Optics and Precision Engineering, 2016, 24(8):2037-2049. (in Chinese)

闫辉, 许廷发, 吴青青, 等.多特征融合匹配的多目标跟踪[J].中国光学, 2013, 6(2):163-170.

YAN H, XU T F, WU Q Q, et al. Multi-object tracking based on multi-feature joint matching[J]. Chinese Optics, 2013, 6(2):163-170. (in Chinese)

高文, 朱明, 贺柏根, 等.目标跟踪技术综述[J].中国光学, 2014, 7(3):365-375.

GAO W, ZHU M, HE B G, et al. Overview of target tracking technology[J]. Chinese Optics, 2014, 7(3):365-375. (in Chinese)

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Detection and segmentation method of surgical instruments based on improved YOLOv5s

Infrared dim-small target detection under complex background based on attention mechanism

3D hand pose estimation algorithm based on cascaded features and graph convolution

Adaptive ship target detection in complex background

Improved SSD based aircraft remote sensing image target detection

Related Author

SONG Zheng

ZHANG Li-ye

WANG Xiao-yu

ZHAO Ji-kang

MENG Xiao-liang

ZHAO Yong-xian

SUN Hai-jiang

LIU Ying

Related Institution

School of Computer Science and Technology， Shandong University of Technology

Changchun Institute of Optics， Fine Mechanics and Physics， Chinese Academy of Sciences

College of Physics and Information Engineering， Fuzhou University

Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China

School of Advanced Manufacturing， Fuzhou University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰