Correlation filter tracking based on feature fusing and model adaptive updating

Wei WANG; Chun-ping WANG; Jun LI; Wei ZHANG

doi:10.3788/OPE.20162408.2059

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Correlation filter tracking based on feature fusing and model adaptive updating

Information Sciences | 更新时间：2020-07-07

- Correlation filter tracking based on feature fusing and model adaptive updating
- Optics and Precision Engineering Vol. 24, Issue 8, Pages: 2059-2066(2016)
- 作者机构：
  
  1.军械工程学院2系, 河北石家庄 050003
  2.中国人民解放军 63813 部队, 海南文昌 571339
  3.中国人民解放军 66285 部队, 河北怀来 571339
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162408.2059
  CLC： TP391
- Received：06 June 2016，
  
  Accepted：24 June 2016，
  
  Published：25 August 2016
- 稿件说明：
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Wei WANG, Chun-ping WANG, Jun LI, et al. Correlation filter tracking based on feature fusing and model adaptive updating[J]. Optics and precision engineering, 2016, 24(8): 2059-2066.
DOI：

Wei WANG, Chun-ping WANG, Jun LI, et al. Correlation filter tracking based on feature fusing and model adaptive updating[J]. Optics and precision engineering, 2016, 24(8): 2059-2066. DOI： 10.3788/OPE.20162408.2059.

摘要

提出了一种基于自适应特征融合和自适应模型更新的相关滤波跟踪算法(CFT)。该算法在跟踪的训练阶段利用损失函数计算特征的自适应权重，在检测阶段对不同特征的响应图进行加权求和，从而实现了响应图层面的自适应特征融合。设计了自适应的模型更新策略，采用响应图的峰值旁瓣比判断是否发生遮挡或错误跟踪，据此决定是否在当前帧更新目标模型。在11个视频序列上对所提算法进行了实验，验证了所采用的自适应特征融合策略和自适应模型更新策略的有效性。与多个传统的采用单特征的相关滤波跟踪算法进行了比较，结果显示，所提算法的跟踪精度和成功率典型值分别提升了18.2%和11.5%。实验结果验证了特征融合和自适应模型更新对跟踪算法的改进具有指导意义。

Abstract

A Correlation Filter based Tracking(CFT) method based on adaptive feature fusing and model adaptive updating is proposed. The proposed method computes the adaptive feature weights by using the loss function of a filter model in training stage

and performs the weighted summing for response maps with multiple features in detection stage. Then it realizes the adaptive feature fusing of response maps. Furthermore

an adaptive model updating strategy is designed to estimate the occlusions and wrong tracking results by utilizing the Peak to Sidelobe Ratio (PSR) of the response maps. The model is updated only when the PSR is small enough. The proposed method is tested on 11 video sequences to verify the validity of the proposed adaptive feature fusing and model adaptive updating strategies. Comparing with other CFT methods by using single feature

the proposed tracker has significantly improvements

by 18.2% in representative precision score and 11.5% in representative success score. Experimental results demonstrate that the adaptive feature fusing strategy and model updating strategy are all effective for improving tracking performance.

关键词

Keywords

references

WU Y, LIM J, and YANG M H. Online object tracking: A benchmark[C].2013 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Portland, USA, 2013: 1354-1362.

YILMAZ A, JAVED O, SHAH M. Object tracking: a survey[J]. ACM Computing Surveys, 2006, 38(4): 1-45.

DECARLO D, METAXAS D. Optical flow constraints on deformable models with applications to face tracking [J]. International Journal of Computer Vision, 2000, 38(2): 99-127.

COMANICIU D, RAMESH V, MEER P. Kernel-based object tracking[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564 -577.

MEI X, LING H B. Robust visual tracking and vehicle classification via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(11): 2259 -2272.

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, Florence, Italy, 2012: 702-715.

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, 32(9): 1627-1645.

FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al..Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.

MARTIN D, FAHAD S K, MICHAEL F, et al.. Adaptive color attributes for real-time visual tracking[C].IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 1090-1097.

MA C, HUANG J B, YANG X K, et al.. Hierarchical convolutional features for visual tracking [C].International Conference on Computer Vision, Santiago, Chile, 2015: 3038-3046.

YANG L, ZHU J K. A scale adaptive Kernel correlation filter tracker with feature integration[C].European Conference on Computer Vision, Zurich, Switzerland, 2014: 254-265.

JIANG Y S, MA J W. Combination features and models for human detection[C].IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 240-248.

WANG N Y, SHI J, YEUNG D, et al.. Understanding and diagnosing visual tracking systems[C]. International Conference on Computer Vision, Santiago, Chile, 2015: 3101-3109.

BOMLE D S, BEVERIDGE J R, DRAPER B A, et al.. Visual object tracking using adaptive correlation filters[C]. IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 2544-2550.

SAM H, AMIR S, PHILIP H. Struck: Structured output tracking with kernels[C]. International Conference on Computer Vision, Barcelona, Spain, 2011: 263-270.

THANG B, NAM V, GERARD G M. Context tracker: Exploring supporters and distracters in unconstrained environments[C].IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, USA, 2011: 1177-1184.

BORIS B, YANG M H, SERGE J B. Visual tracking with online multiple instance learning[C].IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 983-990.

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