融合前景判别和圆形搜索的目标跟踪算法

doi:10.11772/j.issn.1001-9081.2017.11.3128

计算机应用 ›› 2017, Vol. 37 ›› Issue (11): 3128-3133.DOI: 10.11772/j.issn.1001-9081.2017.11.3128

• 第十六届中国机器学习会议(CCML 2017) • 上一篇下一篇

融合前景判别和圆形搜索的目标跟踪算法

林玲鹏^1,2, 黄添强^1,2, 林晶³

1. 福建师范大学软件学院, 福州 350007;
2. 福建省公共服务大数据挖掘与应用工程技术研究中心, 福州 350007;
3. 福建师范大学数学与计算机科学学院, 福州 350007

收稿日期:2017-05-16 修回日期:2017-06-01 出版日期:2017-11-10 发布日期:2017-11-11
通讯作者: 黄添强
作者简介:林玲鹏(1993-),男,福建莆田人,硕士研究生,主要研究方向:机器学习、视频目标跟踪;黄添强(1971-),男,福建莆田人,教授,博士,主要研究方向:机器学习、数据挖掘、多媒体篡改检测;林晶(1992-),女,福建莆田人,硕士研究生,主要研究方向:数据挖掘、视频篡改检测。
基金资助:
国家自然科学基金资助项目（61070062，61502103）；福建省高校产学合作科技重大项目（2015H6007）；福建省高等学校新世纪优秀人才支持计划项目（JAI1038）；福建省科学厅K类基金资助项目（2011007）；福建省教育厅A类基金资助项目（JA10064）；福州市科技计划项目（2014-G-76）。

Object tracking based on foreground discrimination and circle search

LIN Lingpeng^1,2, HUANG Tianqiang^1,2, LIN Jing³

1. Faculty of Software, Fujian Normal University, Fuzhou Fujian 350007, China;
2. Fujian Engineering Research Center of Public Service Big Data Mining and Application, Fuzhou Fujian 350007, China;
3. School of Mathematics and Computer Science, Fujian Normal University, Fuzhou Fujian 350007, China

Received:2017-05-16 Revised:2017-06-01 Online:2017-11-10 Published:2017-11-11
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61070062, 61502103), the Industry-University Cooperation Major Project in Fujian Province (2015H6007), the Program for New Century Excellent Talents in Universities of Fujian Province (JAI1038), the K-Class Foundation Project of Science and Technology Department of Fujian Province (2011007), the A-Class Foundation Project of Education Department of Fujian Province (JA10064), the Science and Technology Program of Fuzhou (2014-G-76).

摘要/Abstract

摘要： 针对运动目标在发生遮挡、形变、旋转和光照等变化时会导致跟踪误差大甚至丢失目标以及传统跟踪算法实时性差的问题，提出了一种融合前景判别和圆形搜索（CS）的目标跟踪算法。该算法采用了图像感知哈希技术来描述与匹配跟踪目标，跟踪过程使用了两种跟踪策略相结合的方法，能够有效地解决上述问题。首先，根据目标运动方向的不确定性和帧间目标运动的缓慢性，通过CS算法搜索当前帧局部（目标周围）最佳匹配位置；然后，采用前景判别PBAS算法搜索当前帧全局最优目标前景；最终，选取两者与目标模板相似度更高者为跟踪结果，并根据匹配阈值判断是否更新目标模板。实验结果表明，所提算法在精度、准确率和实时性上都比MeanShift算法更好，在目标非快速运动时有较好的跟踪优势。

关键词: 目标跟踪, 圆形搜索算法, 前景判别, 感知哈希, 跟踪策略

Abstract: Aiming at the problems of low accuracy and even object lost in moving object tracking under occlusion, deformation, rotation, and illumination changes and poor real-time performance of the traditional tracking algorithm, a target tracking algorithm based on foreground discrimination and Circle Search (CS) was proposed. The image perceptual hashing technique was used to describe and match tracked object, and the tracking process was based on the combination of the above was tracking strategies, which could effectively solve the above problems. Firstly, because the direction of motion uncertain and the inter-frame motion was slow, CS algorithm was used to search the local best matching position (around the tracked object) in the current frame. Then, the foreground discrimination PBAS (Pixel-Based Adaptive Segmenter) algorithm was adopted to search for the global optimal object foreground in the current frame. Finally, the one with higher similarity with the object template was selected as the tracking result, and whether to update the target template was determined according to the matching threshold. The experimental results show that the proposed algorithm is better than the MeanShift algorithm in precision, accuracy, and has a better tracking advantage when the target is not moving fast.

Key words: object tracking, Circle Search (CS) algorithm, foreground discrimination, perceptual hashing, tracking strategy

中图分类号:

TP391.41

林玲鹏, 黄添强, 林晶. 融合前景判别和圆形搜索的目标跟踪算法[J]. 计算机应用, 2017, 37(11): 3128-3133.

LIN Lingpeng, HUANG Tianqiang, LIN Jing. Object tracking based on foreground discrimination and circle search[J]. Journal of Computer Applications, 2017, 37(11): 3128-3133.

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融合前景判别和圆形搜索的目标跟踪算法

Object tracking based on foreground discrimination and circle search

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