Visual tracking of moving objects based on piecewise fusion weight and multi-strategy

SU Yan-zhao; LI Ai-hua; WANG Tao; ZHANG Wei; JIN Guang-zhi

doi:10.3788/OPE.20142212.3409

您当前的位置：

首页 >

文章列表页 >

Visual tracking of moving objects based on piecewise fusion weight and multi-strategy

更新时间：2020-08-13

- Visual tracking of moving objects based on piecewise fusion weight and multi-strategy
- Optics and Precision Engineering Vol. 22, Issue 12, Pages: 3409-3418(2014)
- 作者机构：
  
  第二炮兵工程大学502教研室,陕西西安,710025
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142212.3409
  CLC： TP391
- Received：04 August 2014，
  
  Revised：01 October 2014，
  
  Published：25 December 2014
- 稿件说明：
移动端阅览
苏延召, 李艾华, 王涛等. 结合分段复合权值与多策略的视觉运动目标跟踪[J]. 光学精密工程, 2014,22(12): 3409-3418

SU Yan-zhao, LI Ai-hua, WANG Tao etc. Visual tracking of moving objects based on piecewise fusion weight and multi-strategy[J]. Editorial Office of Optics and Precision Engineering, 2014,22(12): 3409-3418
苏延召, 李艾华, 王涛等. 结合分段复合权值与多策略的视觉运动目标跟踪[J]. 光学精密工程, 2014,22(12): 3409-3418 DOI： 10.3788/OPE.20142212.3409.

SU Yan-zhao, LI Ai-hua, WANG Tao etc. Visual tracking of moving objects based on piecewise fusion weight and multi-strategy[J]. Editorial Office of Optics and Precision Engineering, 2014,22(12): 3409-3418 DOI： 10.3788/OPE.20142212.3409.

摘要

由于视觉监控中运动目标跟踪的准确性易受遮挡、摄像机运动、目标外观变化等因素的影响

本文提出了一种结合分段复合权值与多策略的视觉跟踪算法.该算法首先利用目标、背景以及候选区域特征信息建立分段的复合权值得到目标的位置概率分布.然后结合空间一致性和滞后阈值分割目标位置概率图以进一步抑制噪声干扰

同时通过分析分段复合权值变化判断目标遮挡

调整目标跟踪候选范围

并结合目标历史尺度信息对当前目标尺度进行自适应调整.最后

对目标以及背景区域信息进行动态更新以适应目标外观与场景变化.与典型算法进行的对比实验结果表明:该算法能够有效地应对目标遮挡与摄像机运动等因素的影响

实验时对各组视频的平均处理时间约为10 ms左右

适用于复杂场景条件下运动目标的实时跟踪.

Abstract

As the accuracy of moving object tracking in video surveillance is disturbed by occlusion

camera moving and target appearance changing

an algorithm based on piecewise fusion weight and multi-strategy was proposed. Firstly

the piecewise fusion weight was constructed by combining the feature of object

background and candidate regions to obtain the likelihood image of object location. Then

the likelihood image was segmented with the spatial coherent and hysteresis threshold to suppress noise interference. Meanwhile

the object occlusion was determined and handled by analyzing the change of the piecewise fusion weight and enlarging the candidate area. Furthermore

the object scale was adaptively adjusted according to history and current scales. Finally

object information and background regions were dynamically updated to adapt to the object appearance and scene changing. Experimental results compared with other traditional methods show that the proposed algorithm is applicable to process the moving object tracking in low-contrast scenes in real time

and the average processing time for different video images is 10 ms

which means that the algorithm is suitable for the moving object tracking in complex scenes.

关键词

Keywords

references

FAN J L, SHEN X H, WU Y. Scribble tracker: a matting-based approach for robust tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(8):1633-1644.

YANG H, SHAO L, ZHENG F, et al.. Recent advances and trends in visual tracking: A review [J]. Neurocomputing, 2011, 74(18): 3823-3831.

COMANICIU D, RAMESH V, MEER P. Real-time tracking of non-rigid objects using mean shift[C].Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington DC: IEEE Computer Society, 2000: 142-149.

王丽佳,贾松敏,王爽,等. 采用改进 Mean Shift 算法的移动机器人行人跟踪[J]. 光学精密工程,2013,21(9):2364-2370. WANG L J, JIA S M, WANG SH, et al.. Person tracking of mobile robot using improved Mean Shift [J]. Opt. Precision Eng., 2013, 21(9):2364-2370.

WENG S K, KUO C M, TU S K. Video object tracking using adaptive Kalman filter [J]. Journal of Visual Communication and Image Representation, 2006, 17(6): 1190-1208.

ISARD M, BLAKE A. Condensation-conditional density propagation for visual tracking [J]. International Journal of Computer Vision, 1998, 29 (1):5-28.

宋策,张葆,尹传历. 适于机载环境对地目标跟踪的粒子滤波设计[J]. 光学精密工程,2014,22(4):1037-1047. SONG C, ZHANG B, YIN CH L. Particle filter design for tracking ground targets in airborne environment [J]. Opt. Precision Eng., 2014, 22(4):1037-1047.

龚俊亮,何昕,魏仲慧,等. 采用改进辅助粒子滤波的红外多目标跟踪[J]. 光学精密工程,2012,20(2):413-421. GONG J L, HE X, WEI ZH H, et al.. Multiple infrared target tracking using improved auxiliary particle filter [J]. Opt. Precision Eng., 2012, 20(2):413-421.

COMANICIU D, RAMESH V, MEER P. Kernel-based object tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25 (5):564-577.

COLLINS R, LIU Y, LEORDEANU M. Online selection of discriminative tracking features [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27 (10): 1631-1643.

BIRCHFIELD S T, RANGARAJAN S. Spatiograms versus histograms for region-based tracking[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington DC: IEEE Computer Society, 2005: 1158-1163.

JEYAKAR J, BABU R V, RAMAKRISHNAN K R. Robust object tracking with background-weighted local kernels [J]. Computer Vision and Image Understanding, 2008, 112(3): 296-309.

LI S X, CHANG H X, ZHU C F. Adaptive pyramid mean shift for global real-time visual tracking [J]. Image and Vision Computing, 2010, 28(3): 424-437.

NING J, ZHANG L, ZHANG D, et al.. Robust mean-shift tracking with corrected background-weighted histogram [J]. IET Computer Vision, 2012, 6(1): 62-69.

WANG L, PAN C, XIANG S. Mean-shift tracking algorithm with weight fusion strategy[C]. Proceedings of 18th IEEE International Conference on Image Processing, New York, USA: IEEE Press, 2011: 473-476.

CHOI H S, KIM I S, CHOI J Y. Combining histogram-wise and pixel-wise matchings for kernel tracking through constrained optimization [J]. Computer Vision and Image Understanding, 2014, 118: 61-70.

刘伟宁. 基于DSP+FPGA平台的复杂背景目标检测与跟踪[J]. 液晶与显示,2014, 29(6):1151-1155. LIU W N. Object tracking under complicated background based on DSP+FPGA platform [J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(6):1151-1155.

贺柏根,刘剑,刘伟宁,等. 基于TMS320C6455+FPGA+SDRAM的快速视频跟踪系统设计[J]. 液晶与显示,2014, 29(6):1111-1116. HE B G, LIU J, LIU W N, et al.. Fast image tracking system design based on TMS320C6455+FPGA+SDRAM[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(6):1111-1116.

CHENG Y. Mean shift, mode seeking, and clustering [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1995, 17(8):790-799.

COMANICIU D, MEER P. Mean shift: A robust approach toward feature space analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603-619.

BRADSKI G. Computer vision face tracking for use in a perceptual user interface[C]. Proceedings of IEEE Workshop On Applications of Computer Vision, New Jerseys, USA: IEEE Press, 1998:214-219.

WANG L F, WU H Y, PAN C H. Mean-Shift object tracking with a novel back-projection calculation method[C]. Proceedings of the 9th Asian Conference on Computer Vision-Volume,Berlin,Germany,Springer-Verlag Press, 2009: 83-92.

OTSU N. A threshold selection method from gray-level histogram [J]. IEEE Trans. on Systems, Man, and Cybernetics, 1979, 9: 62-66.

BRINK A D, PENDOCK N E. Minimum cross-entropy threshold selection [J]. Pattern Recognition, 1996, 29(1): 179-188.

NING J, ZHANG L, ZHANG D, et al.. Scale and orientation adaptive mean shift tracking [J]. IET Computer Vision, 2012, 6(1): 52-61.

JIANG Z L, LI S F, GAO D F. An adaptive mean shift tracking method using multiscale images[C]. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition, New Jerseys, USA: IEEE, 2007: 1060-1066.

PENG N S, YANG J, LIU Z. Mean shift blob tracking with kernel histogram filtering and hypothesis testing[J]. Pattern Recognition Letters, 2005, 26(5): 605-614.

FAN J, SHEN X, WU Y. Closed-loop adaptation for robust tracking[C]. Proceedings of the 11th European Conference on Computer Vision, Berlin,Germany,Springer-Verlag Press, 2010: 411-424.

SEVILLA-LARA L, LEARNED-MILLER E. Distribution fields for tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York, USA: IEEE Press, 2012: 1910-1917.

WU Y, LIM J, YANG M H. Online object tracking: A benchmark[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York, USA: IEEE Press, 2013: 2411-2418.

Views

177

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Visual tracking algorithm based on the sparse-regularized subspace

Visual tracking via adaptive interactive fusion

Feature preserving point cloud simplification

Moving object image segmentation by dynamic multi-objective optimization

Moving object tracking with multi-degree-of-freedom based on particle filters

Related Author

Dian-bing CHEN

Hui-li WANG

Hang YANG

Xiu-you WANG

Jian-zhong FAN

Hua-ming LIU

Dong-qing XU

YUAN Xiao-cui

Related Institution

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

University of Chinese Academy of Sciences

School of Computer and Information Engineering, Fuyang Teachers College

School of Computer Science and Technology, Anhui University

School of Mechanical and Electrical Engineering,Nanchang University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@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