高级搜索

基于自适应背景选择和多检测区域的相关滤波算法

蒲磊 冯新喜 侯志强 余旺盛

引用本文: 蒲磊, 冯新喜, 侯志强, 余旺盛. 基于自适应背景选择和多检测区域的相关滤波算法[J]. 电子与信息学报, doi: 10.11999/JEIT190931 shu
Citation:  Lei PU, Xinxi FENG, Zhiqiang HOU, Wangsheng YU. Adaptive Context Selection and Multiple Detection Areas for CorrelationFilter Algorithm[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190931 shu

基于自适应背景选择和多检测区域的相关滤波算法

    作者简介: 蒲磊: 男,1991年生,博士生,研究方向为计算机视觉、目标跟踪;
    冯新喜: 男,1964年生,教授,研究方向为信息融合、模式识别;
    侯志强: 男,1973年生,教授,研究方向为图像处理、计算机视觉;
    余旺盛: 男,1985年生,讲师,研究方向为图像处理、模式识别
    通讯作者: 蒲磊,warmstoner@163.com
  • 基金项目: 国家自然科学基金(61571458, 61703423)

摘要: 为了进一步提高相关滤波算法的判别力和对快速运动、遮挡等复杂场景的应对能力,该文提出一种基于自适应背景选择和多检测区域的跟踪框架。首先对检测后的响应图进行峰值分析,当响应为单峰的时候,提取目标上下左右的4块区域作为负样本对模型进行训练,当响应为多峰的时候,采用峰值提取技术和阈值选择方法提取较大几个峰值区域作为负样本。为了进一步提高算法对遮挡的应对能力,该文提出了一种多检测区域的搜索策略。将该框架和传统的相关滤波算法进行结合,实验结果表明,相对于基准算法,该算法在精度上提高了6.9%,在成功率上提高了6.3%。

English

    1. [1]

      SMEULDERS A W M, CHU D M, CUCCHIARA R, et al. Visual tracking: an experimental survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1442–1468. doi: 10.1109/TPAMI.2013.230

    2. [2]

      HE Anfeng, LUO Chong, TIAN Xinmei, et al. A twofold Siamese network for real-time object tracking[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 4834–4843. doi: 10.1109/CVPR.2018.00508.

    3. [3]

      LI Bo, YAN Junjie, WU Wei, et al. . High performance visual tracking with Siamese region proposal network[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 8971–8980. doi: 10.1109/CVPR.2018.00935.

    4. [4]

      LI Peixia, CHEN Boyu, OUYANG Wanli, et al. GradNet: Gradient-guided network for visual object tracking[C]. Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 6162–6171. doi: 10.1109/ICCV.2019.00626.

    5. [5]

      BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]. Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, US, 2010: 2544–2550. doi: 10.1109/CVPR.2010.5539960.

    6. [6]

      HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]. Proceedings of 12th European Conference on Computer Vision on Computer Vision, Florence, Italy, 2012: 702–715. doi: 10.1007/978-3-642-33765-9_50.

    7. [7]

      HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583–596. doi: 10.1109/tpami.2014.2345390

    8. [8]

      DANELLJAN M, KHAN F S, FELSBERG M, et al. Adaptive color attributes for real-time visual tracking[C]. Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 1090–1097. doi: 10.1109/CVPR.2014.143.

    9. [9]

      DANELLJAN M, HÄGER G, KHAN F S, et al. Convolutional features for correlation filter based visual tracking[C]. Proceedings of 2015 IEEE International Conference on Computer Vision Workshop, Santiago, Chile, 2015: 58–66. doi: 10.1109/ICCVW.2015.84.

    10. [10]

      QI Yuankai, ZHANG Shengping, QIN Lei, et al. Hedged deep tracking[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 4303–4311. doi: 10.1109/CVPR.2016.466.

    11. [11]

      MA Chao, HUANG Jiabin, YANG Xiaokang, et al. Hierarchical convolutional features for visual tracking[C]. Proceedings of 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3074–3082. doi: 10.1109/ICCV.2015.352.

    12. [12]

      WANG Haijun, ZHANG Shengyan, GE Hongjuan, et al. Robust visual tracking via semiadaptive weighted convolutional features[J]. IEEE Signal Processing Letters, 2018, 25(5): 670–674. doi: 10.1109/LSP.2018.2819622

    13. [13]

      QI Yuankai, ZHANG Shengping, QIN Lei, et al. Hedging deep features for visual tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(5): 1116–1130. doi: 10.1109/TPAMI.2018.2828817

    14. [14]

      ZHANG Tianzhu, XU Changsheng, and YANG M H. Learning multi-task correlation particle filters for visual tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(2): 365–378. doi: 10.1109/TPAMI.2018.2797062

    15. [15]

      DANELLJAN M, HÄGER G, KHAN F S, et al. Learning spatially regularized correlation filters for visual tracking[C]. Proceedings of 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 4310–4318. doi: 10.1109/ICCV.2015.490.

    16. [16]

      蒲磊, 冯新喜, 侯志强, 等. 基于空间可靠性约束的鲁棒视觉跟踪算法[J]. 电子与信息学报, 2019, 41(7): 1650–1657. doi: 10.11999/JEIT180780
      PU Lei, FENG Xinxi, HOU Zhiqiang, et al. Robust visual tracking based on spatial reliability constraint[J]. Journal of Electronics &Information Technology, 2019, 41(7): 1650–1657. doi: 10.11999/JEIT180780

    17. [17]

      GALOOGAHI H K, SIM T, LUCEY S. Correlation filters with limited boundaries[C]. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 4630–4638. doi: 10.1109/CVPR.2015.7299094.

    18. [18]

      PU Lei, FENG Xinxi, and HOU Zhiqiang. Learning temporal regularized correlation filter tracker with spatial reliable constraint[J]. IEEE Access, 2019, 7: 81441–81450. doi: 10.1109/ACCESS.2019.2922416

    19. [19]

      LI Feng, TIAN Cheng, ZUO Wangmeng, et al. Learning spatial-temporal regularized correlation filters for visual tracking[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 4904–4913. doi: 10.1109/CVPR.2018.00515.

    20. [20]

      侯志强, 王帅, 廖秀峰, 等. 基于样本质量估计的空间正则化自适应相关滤波视觉跟踪[J]. 电子与信息学报, 2019, 41(8): 1983–1991. doi: 10.11999/JEIT180921
      HOU Zhiqiang, WANG Shuai, LIAO Xiufeng, et al. Adaptive regularized correlation filters for visual tracking based on sample quality estimation[J]. Journal of Electronics &Information Technology, 2019, 41(8): 1983–1991. doi: 10.11999/JEIT180921

    21. [21]

      MUELLER M, SMITH N, GHANEM B, et al. Context-aware correlation filter tracking[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 1396–1404. doi: 10.1109/CVPR.2017.152.

    22. [22]

      WANG Mengmeng, LIU Yong, HUANG Zeyi, et al. Large margin object tracking with circulant feature maps[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 4021–4029. doi: 10.1109/CVPR.2017.510.

    23. [23]

      WU Yi, LIM J, and YANG M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834–1848. doi: 10.1109/TPAMI.2014.2388226

    24. [24]

      DANELLJAN M, HÄGER G, KHAN F S, et al. Accurate scale estimation for robust visual tracking[C]. Proceedings British Machine Vision Conference 2014, Nottingham, UK, 2014: 65.1–65.11. doi: 10.5244/C.28.65.

    25. [25]

      HARE S, GOLODETZ S, SAFFARI A, et al. Struck: Structured output tracking with kernels[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2096–2109. doi: 10.1109/TPAMI.2015.2509974

    26. [26]

      KALAL Z, MIKOLAJCZYK K, and MATAS J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409–1422. doi: 10.1109/TPAMI.2011.239

    27. [27]

      ZHANG Tianzhu, GHANEM B, LIU Si, et al. Robust visual tracking via multi-task sparse learning[C]. Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 2042–2049. doi: 10.1109/CVPR.2012.6247908.

    28. [28]

      BABENKO B, YANG M H, and BELONGIE S. Visual tracking with online multiple instance learning[C]. Proceedings of 2019 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 983–990. doi: 10.1109/CVPR.2009.5206737.

    1. [1]

      陈勇, 刘曦, 刘焕淋. 基于特征通道和空间联合注意机制的遮挡行人检测方法. 电子与信息学报,

    2. [2]

      陈家祯, 吴为民, 郑子华, 叶锋, 连桂仁, 许力. 基于虚拟光学的视觉显著目标可控放大重建. 电子与信息学报,

    3. [3]

      刘政怡, 刘俊雷, 赵鹏. 基于样本选择的RGBD图像协同显著目标检测. 电子与信息学报,

    4. [4]

      黄静琪, 胡琛, 孙山鹏, 高翔, 何兵. 一种基于异步传感器网络的空间目标分布式跟踪方法. 电子与信息学报,

    5. [5]

      孙闽红, 丁辰伟, 张树奇, 鲁加战, 邵鹏飞. 基于统计相关差异的多基地雷达拖引欺骗干扰识别. 电子与信息学报,

    6. [6]

      雷维嘉, 杨苗苗. 时间反转多用户系统中保密和速率优化的预处理滤波器设计. 电子与信息学报,

    7. [7]

      张坤, 水鹏朗, 王光辉. 相参雷达K分布海杂波背景下非相干积累恒虚警检测方法. 电子与信息学报,

    8. [8]

      席博, 洪涛, 张更新. 卫星物联网场景下基于节点选择的协作波束成形技术研究. 电子与信息学报,

  • 图 1  基于响应图峰值提取的自适应背景选择策略

    图 2  多检测区域搜索策略

    图 3  OTB100测试结果的精度曲线和成功率曲线

    图 4  定性分析

    表 1  基于自适应背景选择和多检测区域的相关滤波算法

     输入:图像序列I1, I2, ···, In,目标初始位置p0=(x0, y0)。
     输出:每帧图像的跟踪结果pt=(xt, yt)。
     对于t=1, 2, ···, n, do
      (1) 定位目标中心位置
      (a) 利用前一帧目标位置pt-1确定第t帧ROI区域,并提取
        HOG特征;
      (b) 利用式(3)在多个检测区域进行计算,获得多个响应图;
      (c) 提取多个响应图的最大值作为目标的中心位置pt
      (2) 模型更新
      (a) 对得到的响应图计算峰值个数;
      (b) 当为单峰时,提取上下左右四个背景块进行模型更新;
      (c) 当为多峰时,选取峰值位置的背景块作为负样本,对模型
        进行训练;
      (b) 采用式(7)对模型进行更新。
     结束
    下载: 导出CSV

    表 2  算法跟踪速度对比

    OursDCF_CADCFDSSTTLDMOSSE_CA
    成功率0.5860.5660.5230.5520.4480.488
    跟踪精度0.8080.7760.7390.7310.6330.642
    跟踪速度(FPS)53.582.333328.333.4115
    下载: 导出CSV
  • 加载中
图(4)表(2)
计量
  • PDF下载量:  42
  • 文章访问数:  495
  • HTML全文浏览量:  193
文章相关
  • 通讯作者:  蒲磊, warmstoner@163.com
  • 收稿日期:  2019-11-20
  • 网络出版日期:  2020-06-01
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

/

返回文章