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基于核相关滤波的长期目标跟踪算法

Long-Term Object Tracking Algorithm Based on Kernelized Correlation Filter

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摘要

针对传统核相关滤波器(KCF)无法处理严重遮挡及光照变化等问题, 提出一种结合快速角点检测与双向光流法的长期KCF跟踪算法。首先利用KCF跟踪器在目标位置上提取融合方向梯度直方图特征、颜色属性特征和灰度特征的多通道特征, 计算输出响应图并得到所跟踪目标的峰值旁瓣比(PSR), 然后通过比较PSR与经验阈值来判断目标是否被遮挡; 当目标出现遮挡时, 在快速角点检测的角点基础上利用双向光流法重新检测下一帧目标位置, 并采用一种新模板更新策略来应对严重遮挡。与其他算法进行对比实验, 验证了本文算法对处理遮挡和光照变化具有高效性及稳健性。

Abstract

Focusing on the issue that the traditional kernelized correlation filter (KCF) has poor performance in handing heavy occlusion and illumination variations, a long-term KCF tracking algorithm is proposed combined with fast corner detection and bidirectional optical flow method. First, the KCF tracker is used to extract the multi-channel features of the histogram of gradient, color attributes, and gray features at the target location. The output response map is calculated and the peak sidelobe ratio (PSR) of the tracked target is obtained. The PSR and the empirical threshold determine whether the target is occluded by comparison. When the target is occluded, the bidirectional optical flow method is used to redetect the target position of the next frame based on the corner points detected by the fast corner detection, and a new template updating strategy is adopted to deal with the heavy occlusion. Compared with other algorithms, the proposed algorithm is effective and robust to the processing of occlusion and illumination variations.

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中图分类号:TP391

DOI:10.3788/lop56.010702

所属栏目:傅里叶光学与信号处理

基金项目:国家自然科学基金(60973095)、江苏省产学研联合创新资金—前瞻性联合研究项目(BY2015019-29)

收稿日期:2018-06-13

修改稿日期:2018-06-28

网络出版日期:2018-07-18

作者单位    点击查看

茅正冲:江南大学轻工过程先进控制教育部重点实验室, 江苏 无锡 214122
陈海东:江南大学轻工过程先进控制教育部重点实验室, 江苏 无锡 214122

联系人作者:陈海东(714778302@qq.com)

【1】Kristan M, Matas J, Leonardis A, et al. The visual object tracking VOT2015 challenge results[C]∥IEEE International Conference on Computer Vision Workshop (ICCVW), 2015: 564-586.

【2】Pan Z F, Zhu Y L. Kernelized correlation filters object tracking method with multi-scale estimation[J]. Laser & Optoelectronics Progress, 2016, 53(10): 101501.
潘振福, 朱永利. 多尺度估计的核相关滤波器目标跟踪方法[J]. 激光与光电子学进展, 2016, 53(10): 101501.

【3】Gao M F, Zhang X X. Scale adaptive kernel correlation filtering for target tracking[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041501.
高美凤, 张晓玄. 尺度自适应核相关滤波目标跟踪[J]. 激光与光电子学进展, 2018, 55(4): 041501.

【4】Zhang W, Kang B S. Recent advances in correlation filter-based object tracking: a review[J]. Journal of Image and Graphics, 2017, 22(8): 1017-1033.
张微, 康宝生. 相关滤波目标跟踪进展综述[J]. 中国图象图形学报, 2017, 22(8): 1017-1033.

【5】Kristan M, Pflugfelder R, Leonardis A, et al. The visual object tracking VOT2014 challenge results[C]∥European Conference on Computer Vision, 2014: 191-127.

【6】Bolme D S, Beveridge J R, Draper B A, et al. Visual object tracking using adaptive correlation filters[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010: 2544-2550.

【7】Henriques J F, Caseiro R, Martins P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]∥European Conference on Computer Vision, 2012: 702-715.

【8】Danelljan M, Khan F S, Felsberg M, et al. Adaptive color attributes for real-time visual tracking[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2014: 1090-1097.

【9】Zhu M M, Hu M H. Long-term visual object tracking algorithm based on correlation filter[J]. Journal of Computer Applications, 2017, 37(5): 1466-1470.
朱明敏, 胡茂海. 基于相关滤波器的长时视觉目标跟踪方法[J]. 计算机应用, 2017, 37(5): 1466-1470.

【10】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.

【11】Wang Y C, Huang H, Li S M, et al. Correlation filter tracking based on online detection and scale-adaption[J]. Acta Optica Sinica, 2018, 38(2): 0215002.
王艳川, 黄海, 李邵梅, 等. 基于在线检测和尺度自适应的相关滤波跟踪[J]. 光学学报, 2018, 38(2): 0215002.

【12】van de Weijer J, Schmid C, Verbeek J, et al. Learning color names for real-world applications[J]. IEEE Transactions on Image Processing, 2009, 18(7): 1512-1523.

【13】Wu Y, Lim J, Yang M H. Online object tracking:a benchmark[C]∥IEEE Conference on Computer Vision and Pattern Recognition, 2013: 2411-2418.

【14】Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.

【15】Hare S, Saffari A, Torr P H S. Struck: structured output tracking with kernels[C]∥International Conference on Computer Vision, 2011: 263-270.

引用该论文

Mao Zhengchong,Chen Haidong. Long-Term Object Tracking Algorithm Based on Kernelized Correlation Filter[J]. Laser & Optoelectronics Progress, 2019, 56(1): 010702

茅正冲,陈海东. 基于核相关滤波的长期目标跟踪算法[J]. 激光与光电子学进展, 2019, 56(1): 010702

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