基于颜色和空间信息的多特征融合目标跟踪算法

许婉君, 侯志强, 余旺盛, 张浪

许婉君, 侯志强, 余旺盛, 张浪. 基于颜色和空间信息的多特征融合目标跟踪算法[J]. 应用光学, 2015, 36(5): 755-761. DOI: 10.5768/JAO201536.0502005
引用本文: 许婉君, 侯志强, 余旺盛, 张浪. 基于颜色和空间信息的多特征融合目标跟踪算法[J]. 应用光学, 2015, 36(5): 755-761. DOI: 10.5768/JAO201536.0502005
shu
Xu Wan-jun, Hou Zhi-qiang, Yu Wang-sheng, Zhang Lang. Fusing multi-feature for object tracking algorithm based on color and space information[J]. Journal of Applied Optics, 2015, 36(5): 755-761. DOI: 10.5768/JAO201536.0502005
Citation: Xu Wan-jun, Hou Zhi-qiang, Yu Wang-sheng, Zhang Lang. Fusing multi-feature for object tracking algorithm based on color and space information[J]. Journal of Applied Optics, 2015, 36(5): 755-761. DOI: 10.5768/JAO201536.0502005
shu

基于颜色和空间信息的多特征融合目标跟踪算法

  • 空军工程大学 信息与导航学院,陕西 西安 710077

基金项目: 

国家自然科学基金(61175029;61473309)

详细信息
    通讯作者:

    许婉君(1990-)，女，河南许昌人，硕士研究生，主要从事视觉目标跟踪研究。Email:Xuwanjun901219@163.com

  • 中图分类号: TN911.4; TP391.4

Fusing multi-feature for object tracking algorithm based on color and space information

  • Institute of Information and Navigation,Air Force Engineering University,Xi’an 710077,China

摘要

    摘要
  • 摘要: 为解决单一特征目标跟踪鲁棒性较差的问题，提出一种基于颜色和空间信息的多特征融合目标跟踪算法。采用一种自适应划分颜色区间的方法提取目标颜色特征，利用空间直方图提取目标颜色的空间分布信息。在粒子滤波框架下将自适应颜色直方图和空间直方图相结合，在特征融合中引入特征不确定性度量方法，自适应调整不同特征对跟踪结果的贡献，提高算法的鲁棒性。仿真实验结果表明，该跟踪算法平均位置最小误差值仅6.967 像素，而单一特征跟踪算法以及传统融合算法的跟踪误差达192.576 像素和199.464像素。说明本文算法在跟踪准确性上优于单一特征跟踪算法及传统融合算法，具有更好的跟踪精度和更高的鲁棒性。
    Abstract: Object tracking using single feature often leads to a poor robustness. Aiming at this,an object tracking algorithm using multiple features fusion based on color and space information was presented. In order to enhance the important features, an adaptive method for choosing object color histogram was proposed to get an accurate color model of the object. Meanwhile, spatiograms were used to obtain spatial layout of these colors for the targets. These features were rationally fused in the framework of particle filter. The uncertainty measurement method was then introduced into features fusion to adjust the relative contributions of different features adaptively, and the robustness of the algorithm was significantly enhanced. Simulation experimental results show that the mean minimum location error of the proposed tracking algorithm is only 6.967 pixel,while that of the signal feature tracking algorithm and the traditional algorithm are 192.576 pixel and 199.464 pixel,respectively, which indicates that the proposed algorithm can track objects with better tracking accuracy and robustness.
    • HTML全文
    • 参考文献(1)
  • [1]Wu Yi, Lim J, Yang M H. Online object tracking: a benchmark[C]. Washington, D.C.：IEEE，2013: 2411-2418.
    [2]Yang F, Lu H C, Yang M H. Robust superpixel tracking [J]. IEEE Transactions on Image Processing, 2014, 23(4): 1639-1651.
    [3]Li Wu, Hou Zhiqiang, Wei Guojian, et al. Algorithm of scale-variant objects tracking based on ellipse log-polar transform [J]. Journal of Applied Optics, 2014, 35(1): 65-70.
    李武,侯志强,魏国剑,等. 基于椭圆对数极坐标变换的尺度变化目标跟踪算法[J].应用光学,2014,35(1): 65-70.
    [4]Xin Yang, Cheng K T. Local difference binary for ultrafast and distinctive feature description[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(1):188-194.
    [5]Yan Hui, Xu Tingfa, Wu Qingqing, et al. Multi-object tracking based on multi-feature joint matching[J]. Chinese Optics, 2013,6(2):163-170.
    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.
    [6]Zhong Xiaopin, Xue Jianru, Zheng Nanning, et al. An adaptive fusion strategy based multiple-cue tracking[J]. Journal of Electronics & Information Technology, 2007, 29(5):1017-1022.
    钟小品,薛建儒,郑南宁,等. 基于融合策略自适应的多线索跟踪方法[J]. 电子与信息学报,2007,29(5):1017-1022.
    [7]Wang Yongzhong, Liang Yan, Zhao Chunhui, et al. Kernal-based tracking based on adaptive fusion of multiple cues［J］. Acta Automatica Sinica, 2008,34(4):393-399.
    王永忠，梁彦，赵春晖,等.基于多特征自适应融合的核跟踪方法［J］.自动化学报,2008,34(4):393-399.
    [8]Li Peihua. An improved mean shift algorithm for object tracking[J]. Acta Automatica Sincia, 2007, 33(4): 347-354.
    李培华.一种改进的Mean Shift跟踪算法［J］.自动化学报,2007, 33(4): 347-354.
    [9]Yuan Guowu, Xu Dan.A moving objects tracking algorithm that combins texture and color [J]. Computer Applications and Software, 2011, 28(11):81-84.
    袁国武,徐丹.一种结合了纹理和颜色的运动目标跟踪算法[J].计算机应用与软件,2011,28(11):81-84.
    [10]Birchfield S T, Rangarajan S. Spatiograms versus  histograms for region-based tracking[C]. United States:IEEE, 2005: 1158-1163.
    [11]Gu Xin, Wang Haitao, Wang Lingfeng, et al. Fusing multiple features for object tracking based on uncertainty measurement[J]. Acta Automatica Sinica, 2011, 37(5):550-559.
    顾鑫,王海涛,汪凌峰,等.基于不确定性度量的多特征融合跟踪[J]. 自动化学报,2011,37(5):550-559.
    [12]Liu Qing, Tang Linbo, Zhao Baojun, et al. Infrared target tracking based on adaptive multiple features fusion and mean shift[J]. Journal of Electronics & Information Technology.2012,34(5):1137-1141.
    刘晴,唐林波,赵保军,等. 基于自适应多特征融合的均值迁移红外目标跟踪[J]. 电子与信息学报, 2012, 34(5):1137-1141.
    [13]Katja N, Esther K M, Luc V G. An adaptive color-based filter [J].Image Vision Computing, 2003, 21(1):99-110.
    [14]Yao Zhijun. A new spatiogram similarity measure  -method and its application to object tracking[J]. Journal of Electronics & Information Technology, 2013, 35(7):1644-1649.
    姚志均. 一种新的空间直方图相似性度量方法及其在目标跟踪中的应用［J］.电子与信息学报, 2013,35(7):1644-1649.
    [15]Xu Kun, Yang Xiaojun, Guo Lanying. A multifeature tracking algorithm based on spatial histogram[J]. Computer Engineering and Applications, 2012, 48(19):28-31.
    徐坤，杨小军，郭兰英.一种基于空间直方图的多特征目标跟踪算法［J］.计算机工程与应用，2012,48(19):28-31.
    [16]Zhang K. Real-time compressive tracking[C]. Berlin: Springer-Verlag, 2012: 864-877.
    [17]Xu Wanjun, Hou Zhiqiang, Yu Wangsheng, et al. Fusing multi-feature for object tracking algorithm based on color and space[J]. Journal of Air Force & Engineering University, 2015,16(6):39-43.
    许婉君,侯志强,余旺盛,等.基于背景加权的多特征融合目标跟踪算法[J].空军工程大学学报,2015,16(6):39-43.
    • 相关文章
    • 施引文献(19)

  • 期刊类型引用(8)

    1. 杨翠，刘冲，王海曼，董婷婷，魏雅婷，谷孟丽. 深度神经网络模型用于图像斑点微瑕疵检测. 安庆师范大学学报(自然科学版). 2022(04): 51-56 . 百度学术
    2. 项新建，王乐乐，曾航明. 基于机器视觉的推力轴承垫圈缺陷检测系统研究. 中国测试. 2021(02): 133-139 . 百度学术
    3. 徐建桥，吴俊，陈向成，吴丹超，李兵. 基于规范化样本拆分的轴承缺陷检测. 应用光学. 2021(02): 327-333 . 本站查看
    4. 朱春锦，黎鑫泽，朱建业，张浩. 基于机器视觉的牛奶盒吸管自动检测技术. 南方农机. 2021(10): 163-165 . 百度学术
    5. 李志飞，赵平，杨泽尚. 基于灰度共生矩阵的建筑工地扬尘识别方法. 西安邮电大学学报. 2021(03): 80-83 . 百度学术
    6. 杨萌. 基于红外无损检测的汽车缸体零件缺陷检测方法. 自动化与仪器仪表. 2021(08): 35-39 . 百度学术
    7. 杨晶晶，杨斐. 装甲车体非均匀焊接的红外检测. 兵器材料科学与工程. 2021(05): 131-135 . 百度学术
    8. 郑中华，陈文坚，卢宇，刘伟斌. 一种皮带轮规格测量及微缺陷检测方法研究. 国外电子测量技术. 2021(11): 58-63 . 百度学术

    其他类型引用(11)

    • 资源附件(0)
计量
  • 文章访问数:  1550
  • HTML全文浏览量:  88
  • PDF下载量:  194
  • 被引次数: 19
出版历程

目录

摘要
HTML全文
    参考文献(1)
    相关文章
    施引文献(19)
    资源附件(0)

    /

    返回文章
    返回

    相关数据库

    广告合作

    友情链接

    帮助中心

    扫描关注本刊微信平台

    您是第  2918264位访问者，欢迎！

    地址: 西安市电子三路西段9号 邮政编码: 710065 联系电话: 029-88288172

    版权所有 © 《应用光学》编辑部

    陕公网安备 61011302001501号       陕ICP备19002862号-1

    北京仁和汇智信息技术有限公司 开发  技术支持: info@rhhz.net   百度统计