基于Vibe和时空上下文的运动手势跟踪算法

王民; 石新源; 王稚慧; 李泽洋

doi:10.3788/YJYXS20183301.0092

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基于Vibe和时空上下文的运动手势跟踪算法

图像处理 | 更新时间：2020-07-06

- 基于Vibe和时空上下文的运动手势跟踪算法
- Moving gesture tracking algorithm based on vibe and space-time context
- 液晶与显示 2018年33卷第1期页码：92-98
- 作者机构：
  
  西安建筑科技大学信息与控制工程学院, 陕西西安 710055
- 作者简介：
  
  [ "王民(1959-), 男, 江苏常州人, 教授, 硕士生导师, 主要从事智能信息处理方面的研究工作。E-mail:wangmin1329@163.com" ]
  [ "石新源(1992-), 男, 陕西西安人, 硕士研究生, 主要研究智能信息处理相关技术。E-mail:15529561263@163.com" ]
- 基金信息：
  
  住房城乡建设部科学技术项目计划(2016-R2-045);陕西省教育厅专项基金(2013JK1081);陕西省科学技术研究发展计划项目(CXY1122（2）);陕西省自然科学基金青年基金(2013JQ8003);Housing and Urbsn Ministry of Science project(2016-R2-045);Shaanxi Provincial Department of Education special fund(2013JK1081);Shaanxi Province Science and Technology Research and Development Project(CXY1122(2));Shaanxi Provincial Natural Science Foundation Youth Fund(2013JQ8003)
- DOI：10.3788/YJYXS20183301.0092
  中图分类号： TP391.41
- 收稿日期：2017-06-16，
  
  录用日期：2017-8-28，
  
  纸质出版日期：2018-01-05
- 稿件说明：
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王民, 石新源, 王稚慧, 等. 基于Vibe和时空上下文的运动手势跟踪算法[J]. 液晶与显示, 2018,33(1):92-98.

Min WANG, Xin-yuan SHI, Zhi-hui WANG, et al. Moving gesture tracking algorithm based on vibe and space-time context[J]. Chinese journal of liquid crystals and displays, 2018, 33(1): 92-98.
王民, 石新源, 王稚慧, 等. 基于Vibe和时空上下文的运动手势跟踪算法[J]. 液晶与显示, 2018,33(1):92-98. DOI： 10.3788/YJYXS20183301.0092.

Min WANG, Xin-yuan SHI, Zhi-hui WANG, et al. Moving gesture tracking algorithm based on vibe and space-time context[J]. Chinese journal of liquid crystals and displays, 2018, 33(1): 92-98. DOI： 10.3788/YJYXS20183301.0092.

摘要

针对时空上下文算法易发生漂移以及在目标跟踪丢失后不能重新找回目标的问题，提出了一种融合Vibe前景检测和时空上下文的运动手势跟踪算法。首先使用时空上下文算法对手势预估计并进行干扰检测，当检测到干扰发生时，使用Vibe算法对时空上下文算法的预估计结果进行校准，并更新目标模型。该方法的优势在于，采用无参数模型的Vibe算法校准手势跟踪全过程。实验采用重叠度成功率和中心偏差作为评价体系，实验结果表明，改进算法比原算法跟踪成功率提高60%。该方法增强了运动手势跟踪效果，提高了时空上下文算法的鲁棒性。

Abstract

Aiming at the problem that the space-time context algorithm is prone to drift and can not retrieve the target after target tracking is lost

a moving gesture tracking algorithm combining Vibe foreground detection and space-time context is proposed. Firstly

the space-time context algorithm is used to estimate the gesture and detect the interference. When the interference is detected

the Vibe algorithm is used to calibrate the estimated results of the space-time context algorithm and update the target model. The advantage of this method is that the Vibe algorithm with no parameter model is used to calibrate the whole process of gesture tracking. The experiment adopts overlap success rate and center location errors as the evaluation system. The experimental results show that the improved algorithm is 60% higher than the original algorithm in tracking success rate. This method enhances the motion gesture tracking effect and improves the robustness of the space-time context algorithm.

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Keywords

references

伍玉霞. 基于深度图像信息的手势识别技术研究与应用[D]. 沈阳: 东北大学, 2013.

WU Y X. Research and application of hand gesture recognition technology based on depth image information[D]. Shenyang: Northeastern University, 2013. (in Chinese)

史东承, 倪康.基于YCbCr颜色空间背景建模与手势阴影消除[J].中国光学, 2015, 8(4):589-595.

SHI D C, NI K. Background modeling based on YCbCr color space and gesture shadow elimination[J]. Chinese Optics , 2015, 8(4):589-595. (in Chinese)

王彩虹, 沈燕飞, 王毅, 等.基于时空上下文的手势跟踪与识别[J].计算机工程与应用, 2016, 52(9):202-207.

WANG C H, SHEN Y F, WANG Y, et al . Gesture tracking and recognition based on spatio-temporal context[J]. Computer Engineering and Applications , 2016, 52(9):202-207. (in Chinese)

STARNER T, PENTLAND A. Real-time American Sign Language Recognition From Video Using Hidden Markov Models [M]//SHAH M, JAIN R. Motion - Based Recognition . Dordrecht, Netherlands: Springer, 1997: 227-243.

CORRIDINI A. Dynamic time warping for off-line recognition of a small gesture vocabulary[C]// Proceedings of IEEE ICCV Workshop on Recognition , Analysis , and Tracking of Faces and Gestures in Real-time Systems . Washington, DC, USA: IEEE, 2001: 82-89.

高喆.多重空间特征融合的手势识别[J].小型微型计算机系统, 2016, 37(7):1577-1582.

GAO Z. Hand gesture recognition using multiple spatial features fusion[J]. Journal of Chinese Computer Systems , 2016, 37(7):1577-1582. (in Chinese)

孙保基, 张葆, 宋策, 等.基于角点增强改进的TLD目标跟踪算法[J].液晶与显示, 2016, 31(9):921-928.

SUN B J, ZHANG B, SONG C, et al . Improved TLD object tracking algorithm based on corner reinforced[J]. Chinese Journal of Liquid Crystals and Displays , 2016, 31(9):921-928. (in Chinese)

ZHANG K H, ZHANG L, LIU Q S, et al . Fast Visual Tracking Via Dense Spatio-temporal Context Learning[M]//FLEET D, PAJDLA T, SCHIELE B, et al . Computer Vision - ECCV 2014. Cham: Springer, 2014: 127-141.

张雷, 于凤芹.基于置信图特性的改进时空上下文目标跟踪[J].计算机工程, 2016, 42(8):277-281, 288.

ZHANG L, YU F Q. Improved object tracking via spatial-temporal context based on confidence map property[J]. Computer Engineering , 2016, 42(8):277-281, 288. (in Chinese)

赵龙, 肖军波.一种改进的运动目标抗遮挡跟踪算法[J].北京航空航天大学学报, 2013, 39(4):517-520.

ZHAO L, XIAO J B. Improved algorithm of tracking moving objects under occlusions[J]. Journal of Beijing University of Aeronautics and Astronautics , 2013, 39(4):517-520. (in Chinese)

PÉREZ P, HUE C, VERMAAK J, et al . Color-based probabilistic tracking[C]// Computer Vision - ECCV 2002. Antibes: Springer, 2002, 2350: 661-675.

KWONJ, LEE K M. Tracking by sampling trackers[C]// Proceedings of 2011 IEEE International Conference on Computer Vision . Barcelona: IEEE, 2011: 1195-1202.

KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2012, 34(7):1409-1422.

GRABNER H, GRABNER M, BISCHOF H. Real-time tracking via on-line boosting[C]// Proceedings of the British Machine Conference . Edinburgh: British Machine Vision Association, 2006: 6.

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