自步上下文感知的相关滤波跟踪算法

doi:10.3969/j.issn.1006-2475.2018.11.007

计算机与现代化

自步上下文感知的相关滤波跟踪算法

（河海大学计算机与信息学院，江苏南京211100）

收稿日期:2018-05-18 出版日期:2018-11-22 发布日期:2018-11-23
作者简介:张驰（1993-），男，上海人，河海大学计算机与信息学院硕士研究生，研究方向：机器学习，目标跟踪；韩立新（1967-），男，江苏南京人，教授，博士生导师，研究方向：信息检索，数据挖掘，模式识别；徐国夏（1994-），男，江西上饶人，硕士研究生，研究方向：图匹配，目标跟踪。

Self-paced Context-aware Correlation Filter Tracking Algorithm

（College of Computer and Information, Hohai University, Nanjing 211100， China）

Received:2018-05-18 Online:2018-11-22 Published:2018-11-23

摘要/Abstract

摘要： 针对目标跟踪中的目标尺度变换、遮挡、快速运动等问题，提出自步上下文感知的相关滤波跟踪算法。首先在正则化最小二乘分类器中引入目标的全局上下文信息，使得这些上下文信息能够被滤波器所学到，并对目标产生高响应，对上下文信息接近零响应；然后引入自步学习，给每一帧的目标和上下文信息赋予权重，挑选出可靠的目标和上下文信息，更新滤波模板；最后学习得到稳健和高效的外观模型。实验表明本文算法在距离精度（DP）提高了2.81%，成功率（SR）提高了13.9%，具有较好的跟踪效果。

关键词: 目标跟踪, 相关滤波, 上下文感知, 自步学习

Abstract: Aiming at the problem of target scaling, occlusion and fast movement in target tracking, this paper proposes a self-paced context-aware correlation filter tracking algorithm. First, the global context information of the target is introduced in the regularized least squares classifier so that these context information can be learned by the filter, and a high response to the target and a near-zero response to the context information. Then we introduce self-paced learning, assign weights to the target and context information of each frame, pick out reliable target and context information, and update the filter template. Finally a robust and efficient appearance model is got by learning. Experiments show that the algorithm improves 2.81% in distance accuracy (DP), improves success rate (SR) by 13.9%, and has a good tracking effect.

Key words: target tracking, correlation filtering, context-aware, self-paced learning

中图分类号:

TP391.9

张驰，韩立新，徐国夏. 自步上下文感知的相关滤波跟踪算法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2018.11.007.

ZHANG Chi, HAN Li-xin, XU Guo-xia. Self-paced Context-aware Correlation Filter Tracking Algorithm[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.11.007.

参考文献

［1］ WALIA G S, KAPOOR R. Recent advances on multicue object tracking: A survey［J］. Artificial Intelligence Review, 2016,46(1):1-39.
［2］ PAN Z, LIU S, FU W N. A review of visual moving target tracking［J］. Multimedia Tools & Application, 2017,76(16):16989-17018.
［3］ CHEN Z, HONG Z B, TAO D C. An experimental survey on correlation filter-based tracking［J］. Computer Science, 2015, 53(6025):68-83.
［4］ BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters［C］//IEEE Computer Vision and Pattern Recognition. 2010:2544-2550.
［5］ RUI C, MARTINS P, BATISTA J. Exploiting the circulant structure of tracking-by-detection with kernels［C］// European Conference on Computer Vision(ECCV). Springer, 2012:702-715.
［6］ HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters［J］. Pattern Analysis and Machine Intelligence, 2015,37(3):583-596.
［7］ DANELLJAN M, HAGER G, KHAN F S, et al. Convolutional features for correlation filter based visual tracking［C］// IEEE International Conference on Computer Vision(ICCV). 2015:621-629.
［8］ LI Y, ZHU J K. A scale adaptive kernel correlation filter tracker with feature integration［C］// European Conference on Computer Vision. Springer, 2014:254-265.
［9］ GALOOGAHI H K, FAGG A, LUCEY S. Learning background-aware correlation filters for visual tracking［C］// IEEE Computer Vision and Pattern Recognition. 2017:1144-1152.
［10］KUMAR M P, PACKER B, KOLLER D. Self-paced learning for latent variable models［C］// International Conference on Neural Information Processing Systems. 2010:1189-1197.
［11］DANELLJAN M, HGER G, KHAN F S, et al. Accurate scale estimation for robust visual tracking［C］// British Machine Vision Conference. 2014:65.1-65.11.
［12］JIANG L, MENG D Y, YU S I, et al. Self-paced learning with diversity［J］. Advances in Neural Information Processing Systems, 2014:2078-2086.
［13］HUANG W H, GU J S, MA X, et al. Correlation filter-based self-paced object tracking［C］// IEEE International Conference on Robotics and Automation. 2017:4437-4442.
［14］WU Y, LIM J ,YANG M H. Online object tracking: A benchmark ［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2013:2411-2418.
［15］WU Y, LIM J ,YANG M H. Object tracking benchmark［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(9):1834-1848.

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自步上下文感知的相关滤波跟踪算法

Self-paced Context-aware Correlation Filter Tracking Algorithm

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