基于相关滤波融合多特征的运动目标跟踪方法
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昆明理工大学信息工程与自动化学院,昆明,650500

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国家自然科学基金 61462052国家自然科学基金(61462052)资助项目。


Object Tracking Combining Multiple Features Based on Correlation Filter
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Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming,650500,China

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    摘要:

    针对复杂环境下仅使用单一图像特征跟踪精度和鲁棒性差的问题,提出一种多特征融合的相关滤波目标跟踪算法。该算法首先从目标和背景区域分别提取方向梯度直方图(Histogram of oriented gradient,HOG)特征、颜色直方图特征和卷积特征,采用固定权重方法融合HOG特征和颜色直方图特征的特征响应图,然后将该层融合结果与卷积特征响应图采用自适应权重融合策略进行融合,基于融合后的响应图估计出目标位置,并采用尺度估计方法解决目标尺度变化问题,最后采用稀疏模型更新策略进行模型更新。在OTB-2013公开标准测试集中验证本文算法性能,并与主流的目标跟踪算法进行了对比分析。实验结果表明,与其中最优算法相比,本文算法的平均距离精度值和平均重叠精度值都有所提高。本文算法由于有效地利用了HOG特征、颜色直方图特征和卷积特征,在复杂场景下目标跟踪的准确性和鲁棒性都优于其他算法。

    Abstract:

    Aiming at the problem that object tracking with single image feature under complex circumstances has low accuracy and poor robustness, a correlation filtering object tracking algorithm based on multi-feature fusion is proposed. Firstly, histogram of oriented gradient (HOG) features, color histogram features and convolutional features are respectively extracted from the target and background regions, and a fixed-coefficient fusion strategy is adopted to combine the feature response maps of HOG features and color histogram features. Then the fused response map and the convolutional features response map are fused by adaptive weighted fusion strategy,and the scale estimation algorithm is used to solve the problem of target scale changes. Finally, the sparse model update strategy is used to update the model. The proposed algorithm is evaluated on OTB-2013 dataset and compared with state-of-the-arts object tracking algorithms. Extensive experimental results show that our method significantly improves the performance in median distance precision and median overlap precision compared to the optimal algorithm. The accuracy and robustness of the proposed algorithm are superior to those of other algorithms in complex scenarios because of the effective use of HOG, color histogram and convolutional features.

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谢柳,尚振宏,刘辉.基于相关滤波融合多特征的运动目标跟踪方法[J].数据采集与处理,2019,34(1):122-134

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  • 收稿日期:2018-09-18
  • 最后修改日期:2018-12-21
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  • 在线发布日期: 2019-04-12