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融合显著性与运动信息的相关滤波跟踪算法

张伟俊 钟胜 徐文辉 WU Ying

张伟俊,  钟胜,  徐文辉,  WU Ying.  融合显著性与运动信息的相关滤波跟踪算法.  自动化学报,  2021,  47(7): 1572−1588 doi: 10.16383/j.aas.c190122
引用本文: 张伟俊,  钟胜,  徐文辉,  WU Ying.  融合显著性与运动信息的相关滤波跟踪算法.  自动化学报,  2021,  47(7): 1572−1588 doi: 10.16383/j.aas.c190122
Zhang Wei-Jun,  Zhong Sheng,  Xu Wen-Hui,  Wu Ying.  Correlation filter based visual tracking integrating saliency and motion cues.  Acta Automatica Sinica,  2021,  47(7): 1572−1588 doi: 10.16383/j.aas.c190122
Citation: Zhang Wei-Jun,  Zhong Sheng,  Xu Wen-Hui,  Wu Ying.  Correlation filter based visual tracking integrating saliency and motion cues.  Acta Automatica Sinica,  2021,  47(7): 1572−1588 doi: 10.16383/j.aas.c190122

融合显著性与运动信息的相关滤波跟踪算法

doi: 10.16383/j.aas.c190122
基金项目: 国家重点研发计划(2016YFF0101502)资助
详细信息
    作者简介:

    张伟俊:华中科技大学人工智能与自动化学院博士研究生. 2012年获得华中科技大学电子信息工程系学士学位. 主要研究方向为计算机视觉,模式识别.本文通信作者. E-mail: starfire.zhang@gmail.com

    钟胜:华中科技大学人工智能与自动化学院教授. 2005年获得华中科技大学模式识别与智能系统博士学位. 主要研究方向为模式识别, 图像处理, 实时嵌入式系统. E-mail: zhongsheng@hust.edu.cn

    徐文辉:华中科技大学人工智能与自动化学院博士研究生. 2006年获得吉林大学大学学士学位. 主要研究方向为计算机视觉, 算法加速. E-mail: xuwenhui@hust.edu.cn

    WU Ying:美国西北大学电子工程与计算机系终身正教授. 2005年获得美国伊利诺伊大学厄巴纳−香槟分校电子与计算工程博士学位. 主要研究方向为计算视觉与图形学, 图像与视频处理, 多媒体, 机器学习, 人体运动, 人机智能交互, 虚拟现实. E-mail: yingwu@ece.northwestern.edu

  • 收稿日期 2019-03-03 录用日期 2019-07-30 Manuscript received March 3, 2019; accepted July 30, 2019 国家重点研发计划 (2016YFF0101502) 资助 Supported by the National Key Research and Development Program of China (2016YFF0101502) 本文责任编委 赖建煌 Recommended by Associate Editor LAI Jian-Huang 1. 华中科技大学人工智能与自动化学院 武汉 430074 中国 2. 华中科技大学多谱信息处理技术国家级重点实验室 武汉 430074 中国 3. 美国西北大学电子工程与计算机系 埃文斯顿 伊利诺伊州 60208 美国 1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China 2. Na-tional Key Laboratory of Science and Technology on MultiSpectral Information Processing, Huazhong University of Science and Technology, Wuhan 430074, China  3. Department of Elec-
  • trical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA

Correlation Filter Based Visual Tracking Integrating Saliency and Motion Cues

Funds: Supported by the National Key Research and Development Program of China (2016YFF0101502)
More Information
    Author Bio:

    ZHANG Wei-Jun Ph. D. candidate at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology (HUST). He received his bachelor degree from Huazhong University of Science and Technology in 2012. His research interest covers computer vision and pattern recognition. Corresponding author of this paper

    ZHONG Sheng Professor at the School of Automation, Huazhong University of Science and Technology (HUST). He received the Ph. D. degree in pattern recognition and intelligent systems in 2005 from Huazhong University of Science and Technology. His research interest covers pattern recognition, image processing, and real-time embedded systems

    XU Wen-Hui Ph. D. candidate at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. He received his bachelor degree from Jilin University in 2006. His research interest covers computer vision and accelerated computing

    WU Ying Professor (tenured) in the Department of Electrical Engineering and Computer Science, Northwestern University, USA. He received his Ph. D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign (UIUC) in 2001. His research interest covers computer vision/graphics, image/video processing, multi-media, machine learning, human motion, human-computer intelligent interaction, and virtual environments

  • 摘要:

    主流的目标跟踪算法以矩形模板的形式建立被跟踪物体的视觉表征, 无法有效区分目标与背景像素, 在背景复杂、目标非刚体形变、复杂运动等挑战性因素影响下容易出现模型偏移的问题, 导致跟踪失败. 与此同时, 像素级的显著性信息与运动先验信息作为人类视觉系统有效区分目标与背景、识别运动物体的重要信号, 并没有在主流目标跟踪算法中得到有效的集成利用. 针对上述问题, 提出目标的像素级概率性表征模型, 并且建立与之对应的像素级目标概率推断方法, 能够有效利用像素级的显著性与运动观测信息, 实现与主流的相关滤波跟踪算法的融合; 提出基于显著性的观测模型, 通过背景先验与提出的背景距离模型, 能够在背景复杂的情况下得到高辨识度的像素级图像观测; 利用目标与相机运动的连续性来计算目标和背景的运动模式, 并以此为基础建立基于运动估计的图像观测模型. 实验结果表明, 提出的目标表征模型与融合方法能够有效集成上述像素级图像观测信息, 提出的跟踪方法总体跟踪精度优于多种当下最先进的跟踪器, 对跟踪场景中的背景复杂、目标形变、平面内旋转等挑战性因素具有更好的鲁棒性.

    1)  收稿日期 2019-03-03 录用日期 2019-07-30 Manuscript received March 3, 2019; accepted July 30, 2019 国家重点研发计划 (2016YFF0101502) 资助 Supported by the National Key Research and Development Program of China (2016YFF0101502) 本文责任编委 赖建煌 Recommended by Associate Editor LAI Jian-Huang 1. 华中科技大学人工智能与自动化学院 武汉 430074 中国 2. 华中科技大学多谱信息处理技术国家级重点实验室 武汉 430074 中国 3. 美国西北大学电子工程与计算机系 埃文斯顿 伊利诺伊州 60208 美国 1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China 2. Na-tional Key Laboratory of Science and Technology on MultiSpectral Information Processing, Huazhong University of Science and Technology, Wuhan 430074, China  3. Department of Elec-
    2)  trical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
  • 图  1  总体跟踪流程图

    Fig.  1  Overall tracking procedure

    图  2  像素级目标概率推断模型的贝叶斯网络示意图

    Fig.  2  Bayesian network representation of pixel-level target probabilistic inference model

    图  3  基于颜色与基于显著性的目标似然概率估计结果对比

    Fig.  3  Results of color-based and saliency-based target likelihood estimation

    图  4  基于目标与背景运动模型的似然概率估计示意图

    Fig.  4  Demonstration of likelihood estimation based on motion models of target and background

    图  5  本文提出的跟踪算法和 DSST[23]、SRDCF[24]、ACFN[18]、CFNet[17]在 8 个典型 OTB 序列上的跟踪结果 (从上往下分别是 David、Singer2、Doll、Bolt、Soccer、Panda、Diving 和 MotorRolling 序列)

    Fig.  5  Tracking results using our proposed method compared with DSST, SRDCF, ACFN and CFNet on 8 OTB image sequences (From top to down: David, Singer2, Doll, Bolt, Soccer, Panda, Diving and MotorRolling

    图  6  在 OTB-100 数据集上的一次通过估计曲线

    Fig.  6  One-pass-evaluation (OPE) curves on OTB-100 dataset

    图  7  在 OTB-100 数据集不同挑战性因素影响下的成功率图

    Fig.  7  Success plots on sequences with different challenging attributes on OTB-100 dataset

    图  8  在 VOT2018 数据集上的实验结果

    Fig.  8  Experimental results on VOT2018 dataset

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