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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

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, 2020, 42(12): 3061-3067. doi: 10.11999/JEIT190931
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, 2020, 42(12): 3061-3067. doi: 10.11999/JEIT190931
Lei PU, Xinxi FENG, Zhiqiang HOU, Wangsheng YU. Correlation Filter Algorithm Based on Adaptive Context Selection and Multiple Detection Areas[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3061-3067. doi: 10.11999/JEIT190931
Citation: Lei PU, Xinxi FENG, Zhiqiang HOU, Wangsheng YU. Correlation Filter Algorithm Based on Adaptive Context Selection and Multiple Detection Areas[J]. Journal of Electronics & Information Technology, 2020, 42(12): 3061-3067. doi: 10.11999/JEIT190931

基于自适应背景选择和多检测区域的相关滤波算法

doi: 10.11999/JEIT190931
基金项目: 国家自然科学基金(61571458, 61703423)
详细信息
    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

  • 中图分类号: TN911.73; TP391.4

Correlation Filter Algorithm Based on Adaptive Context Selection and Multiple Detection Areas

Funds: The National Natural Science Foundation of China (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%。
  • 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4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;
      (d) 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

    本文算法DCF_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)53.582.3333.028.333.4115.0
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-11-20
  • 修回日期:  2020-05-26
  • 网络出版日期:  2020-06-01
  • 刊出日期:  2020-12-08

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