华南理工大学学报(自然科学版) ›› 2020, Vol. 48 ›› Issue (1): 42-50.doi: 10.12141/j.issn.1000-565X.190173

• 计算机科学与技术 • 上一篇    下一篇

二叉树模型在目标跟踪中的应用

郑运平 李睿君   

  1. 华南理工大学 计算机科学与工程学院,广东 广州 510006
  • 收稿日期:2019-04-04 修回日期:2019-04-28 出版日期:2020-01-25 发布日期:2019-12-01
  • 通信作者: 李睿君,主要从事图像处理与模式识别研究。 E-mail:230731704@qq.com
  • 作者简介:郑运平 (1979-),男,博士,副教授,主要从事图像处理与模式识别研究。E-mail: zhengyp@ scut. edu. cn
  • 基金资助:
    国家自然科学基金资助项目 (61300134); 广东省自然科学基金资助项目 (2017A030313349,2015A030313206,S2013010012515); 高等学校博士学科点专项科研基金新教师类资助课题 (20120172120036); 广东高校优秀青年创新人才培养计划项目 (LYM11015); 华南理工大学中央高校基本科研业务费专项资金资助项目 (2015ZM133); 国家留学基金资助出国留学项目 (201406155015); 广东省大学生创新创业训练计划项目 (S201910561235)

Application of Binary Tree Model in Object Tracking

ZHENG Yunping LI Ruijun   

  1. School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,Guangdong,China
  • Received:2019-04-04 Revised:2019-04-28 Online:2020-01-25 Published:2019-12-01
  • Contact: 李睿君,主要从事图像处理与模式识别研究。 E-mail:230731704@qq.com
  • About author:郑运平 (1979-),男,博士,副教授,主要从事图像处理与模式识别研究。E-mail: zhengyp@ scut. edu. cn
  • Supported by:
    Supported by the National Natural Science Foundation of China (61300134),the Natural Science Foundation of Guangdong Province (2017A030313349,2015A030313206,S2013010012515) and the State Scholarship Found of China (201406155015)

摘要: 目标跟踪一直是计算机视觉领域的重要研究课题,广泛应用于视频监控、交通监视、医学诊断等领域。文中提出了一种基于二叉树模型的目标跟踪算法,该方法通过二叉树分块,将图像的目标区域分割为若干大小不一的同类块,块内像素相近,可用一个值或向量统一表示; 块间像素差距较大,从而构成整个目标的特征描述模型。并从准确性和跟踪速度两个方面对 CT 算法、基于四叉树模型的算法 (QT 算法) 和提出的基于二叉树模型的算法 (BT 算法) 进行了比较,结果表明: 与基于四叉树模型的算法相比,基于二叉树模型的跟踪算法在准确性方面几乎不受影响的前提下,跟踪速度显著提升; 与以跟踪速度快闻名的判别式 CT 算法相比,在跟踪速度大致相当的前提下,跟踪准确性却更好。

关键词:  二叉树模型, 四叉树模型, 目标跟踪, 图像分块, 准确性, 跟踪速度

Abstract: Object tracking has always been an important research topic in the field of computer vision. It is widely used in video surveillance,traffic monitoring,medical diagnosis and other fields. An object tracking algorithm based on binary tree model was proposed. The method divides the target area of the image into several homogene-ous blocks of different sizes,following the rule of the binary tree partition. The pixels in the block are similar and can be represented by a single value or vector whereas the pixels in different blocks differ from each significantly,thus forming the feature description model of the whole object. The CT algorithm,the quadtree model based algo-rithm (QT algorithm) and the proposed binary tree model based algorithm (BT algorithm) were compared from the aspects of accuracy and tracking speed. The results show that compared with the quadtree-based algorithm,the BT-based tracking algorithm can improve the tracking speed significantly without reducing the tracking accuracy.And compared with the discriminant CT-based algorithm,which is known for its fast tracking speed,the BT-based tracking accuracy is even better under the premise that the tracking speed is roughly equal.

Key words: binary tree model, quadtree model, object tracking, image segmentation, accuracy, tracking speed