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

Lei PU; Xinxi FENG; Zhiqiang HOU; Wangsheng YU

doi:10.11999/JEIT190931

Volume 42 Issue 12

Dec. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(12): 3061-3067

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

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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

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

doi: 10.11999/JEIT190931

1.
Graduate College, Air Force Engineering University, Xi’an 710077, China
2.
Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
3.
School of Computer Science and Technology, Xian University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (61571458, 61703423)

Received Date: 2019-11-20
Rev Recd Date: 2020-05-26

Available Online: 2020-06-01

Publish Date: 2020-12-08

Abstract

Abstract

In order to improve further the discrimination ability of the correlation filtering algorithm and the ability to deal with fast motion and occlusion, a tracking framework based on adaptive context selection and multiple detection areas is proposed. Firstly, the peak value of the detected response map is analyzed. When the response is single peak, four areas surrounding the target are extracted as negative samples to train the model. When the response is multi-peak, the peak value extraction technology and threshold selection are used to extract several larger peak areas as negative samples. In order to improve further the ability to deal with occlusion, a multi detection area search strategy is proposed. Combining the framework with the traditional correlation filter algorithm, the experimental results show that the proposed algorithm improves the accuracy by 6.9% and the success rate by 6.3%.
- Visual tracking,
- Correlation filter,
- Occlusion,
- Context selection

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References(28)

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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