[1]戴煜彤,陈志国,傅毅.相关滤波的运动目标抗遮挡再跟踪技术[J].智能系统学报,2021,16(4):630-640.[doi:10.11992/tis.202005027]
DAI Yutong,CHEN Zhiguo,FU Yi.Anti-occlusion retracking technology for a moving target based on correlation filtering[J].CAAI Transactions on Intelligent Systems,2021,16(4):630-640.[doi:10.11992/tis.202005027]
点击复制
DAI Yutong,CHEN Zhiguo,FU Yi.Anti-occlusion retracking technology for a moving target based on correlation filtering[J].CAAI Transactions on Intelligent Systems,2021,16(4):630-640.[doi:10.11992/tis.202005027]
相关滤波的运动目标抗遮挡再跟踪技术
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第4期
页码:
630-640
栏目:
学术论文—机器学习
出版日期:
2021-07-05
- Title:
- Anti-occlusion retracking technology for a moving target based on correlation filtering
- Keywords:
- object tracking; correlation filter; multi-feature fusion; ULBP; Gaussian mask; peak-to-average ratio; Kalman prediction; anti-occlusion
- 分类号:
- TP391.41
- 摘要:
- 针对相关滤波在抗遮挡方面效果不佳的问题，本文在ECO_HC(efficient convolution operators handcraft)的基础上提出了一种多特征融合的抗遮挡相关滤波算法。在相关滤波算法的框架下，将目标ULBP(uniform local binary pattern)纹理特征和目标HOG(histogram of oriented gridients)特征进行线性加权融合；在模型建立与更新阶段通过高斯掩码函数缓解循环移位造成的边界效应；通过计算目标最大响应值的峰值均值比来判断目标状态，并将卡尔曼算法作为目标被遮挡后重定位策略。实验结果显示，在16个视频序列上，该文算法的平均精确度达到87.3%，成功率达到76.5%，相比基线算法，分别提升了27.7%和23.7%。
- Abstract:
- To address the poor anti-occlusion effect of correlation filtering, this paper proposes an anti-occlusion correlation filtering algorithm by means of multifeature fusion based on efficient convolution operators handcraft. First, based on the framework of correlation filtering, a method of linearly weighted fusion is adopted to deal with the target uniform local binary pattern texture feature and the target histogram of oriented gradients feature. Second, the Gaussian mask function is used during the model establishment and update phase to ease the boundary effect caused by cyclic shift. Lastly, the target state is judged by calculating the peak-to-average ratio of the target maximum response value, and the Kalman algorithm is utilized as the relocation strategy after the target is blocked. Experimental results show that the average accuracy of the proposed algorithm reaches 87.3%, and the success rate reaches 76.5% on 16 test sequences, which are 27.7% and 23.7% higher than those of the baseline algorithm, respectively.
- 参考文献/References:
-
[1] 吴小俊, 徐天阳, 须文波. 基于相关滤波的视频目标跟踪算法综述[J]. 指挥信息系统与技术, 2017, 8(3): 1-5
WU Xiaojun, XU Tianyang, XU Wenbo. Review of target tracking algorithms in video based on correlation filter[J]. Command information system and technology, 2017, 8(3): 1-5
[2] 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
ZHANG Wei, KANG Baosheng. Recent advances in correlation filter-based object tracking: a review[J]. Journal of image and graphics, 2017, 22(8): 1017-1033
[3] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 2010: 2544-2550.
[4] 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.
[5] DANELLJAN M, H?GER G, KHAN F S, et al. Accurate scale estimation for robust visual tracking[C]//British Machine Vision Conference. Nottingham, UK: BMVA Press, 2014.
[6] DANELLJAN M, H?GER G, SHAHBAZ KHAN F, et al. Learning spatially regularized correlation filters for visual tracking[C]//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 4310-4318.
[7] DANELLJAN M, ROBINSON A, KHAN F S, et al. Beyond correlation filters: learning continuous convolution operators for visual tracking[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016: 472-488.
[8] DANELLJAN M, BHAT G, SHAHBAZ KHAN F, et al. ECO: efficient convolution operators for tracking[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 6638-6646.
[9] LI Bo, YAN Junjie, WU Wei, et al. High performance visual tracking with Siamese region proposal network[C]//Proceedings of 2018 IEEE/CVF Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 6638-6646.
[10] LI Bo, WU Wei, WANG Qiang, et al. SiamRPN++: evolution of Siamese visual tracking with very deep networks[C]//Proceedings of 2019 Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019.
[11] FAN Heng, LIN Liting, YANG Fan, et al. LaSOT: a high-quality benchmark for large-scale single object tracking[C]//Proceedings of 2019 IEEE/CVF Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019.
[12] HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the Circulant structure of tracking by detection with kernels[C]//Proceedings of the 12th European Conference on Computer Vision. Florence, Italy: Springer, 2012: 702-715.
[13] COMANICIU D, MEER P. Mean shift: a robust approach toward feature space analysis[J]. IEEE transactions on pattern analysis and machine intelligence, 2002, 24(5): 603-619.
[14] MEI Xue, LING Haibin. Robust visual tracking and vehicle classification via sparse representation[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 33(11): 2259-2272.
[15] GRABNER H, GRABNER M. BISCHOF M. Real-time tracking via on-line boosting[C]//Proceedings of 2006 British Machine Vision Conference. Edinburgh, UK: BMVC, 2006: 47-56.
[16] DANELLJAN M, SHAHBAZ KHAN F, FELSBERG M, et al. Adaptive color attributes for real-time visual tracking[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA: IEEE, 2014: 1090-1097.
[17] BERTINETTO L, VALMADRE J, GOLODETZ S, et al. Staple: complementary learners for real-time tracking[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 1401-1409.
[18] VALMADRE J, BERTINETTO L, HENRIQUES J, et al. End-to-end representation learning for correlation filter based tracking[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 2805-2813.
[19] 包晓安, 詹秀娟, 王强, 等. 基于KCF和SIFT特征的抗遮挡目标跟踪算法[J]. 计算机测量与控制, 2018, 26(5): 148-152
BAO Xiaoan, ZHAN Xiujuan, WANG QIANG, et al. Anti occlusion target tracking algorithm based on KCF and SIFT feature[J]. Computer measurement and control, 2018, 26(5): 148-152
[20] 闫河, 张杨, 杨晓龙, 等. 一种抗遮挡核相关滤波目标跟踪算法[J]. 光电子·激光, 2018, 29(6): 647-652
YAN He, ZHANG Yang, YANG Xiaolong, et al. A kernelized correlaton filters with occlusion handling[J]. Journal of optoelectronics·laser, 2018, 29(6): 647-652
[21] FARAGHER R. Understanding the basis of the Kalman filter via a simple and intuitive derivation [Lecture Notes][J]. IEEE signal processing magazine, 2012, 29(5): 128-132.
[22] LI Feng, TIAN Cheng, ZUO Wangmeng, et al. Learning spatial-temporal regularized correlation filters for visual tracking[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 4904-4913.
[23] LI Yang, ZHU Jianke, HOI S C H, et al. Robust estimation of similarity transformation for visual object tracking[C]//Proceedings of the 3rd AAAI Conference on Artificial Intelligence, AAAI 2019, The 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. Honolulu, Hawaii, USA: AAAI, 2019: 8666-8673.
[24] LI Yang, ZHU Jianke. A scale adaptive kernel correlation filter tracker with feature integration[C]//Proceedings of 2014 European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014: 254-265.
- 相似文献/References:
-
[1]王绍钰 蔡自兴,陈爱斌.改进的粒子滤波器目标跟踪方法[J].智能系统学报,2008,3(3):189.
WANG Shao-yu,CAI Zi-xing,CHEN Ai-bin.Improved object tracking method for particle filters[J].CAAI Transactions on Intelligent Systems,2008,3():189.
[2]刘 清,吴志刚,窦 琴,等.粒子滤波的视频目标跟踪算法研究[J].智能系统学报,2009,4(6):538.[doi:10.3969/j.issn.1673-4785.2009.06.012]
LIU Qing,WU Zhi-gang,DOU Qin,et al.A particle filtering algorithm for tracking moving objects in videos[J].CAAI Transactions on Intelligent Systems,2009,4():538.[doi:10.3969/j.issn.1673-4785.2009.06.012]
[3]伍 明,孙继银.一种机器人未知环境下动态目标跟踪交互多模滤波算法[J].智能系统学报,2010,5(2):127.
WU Ming,SUN Ji-yin.An interacting multiple model filtering algorithm for mobile robots to improve tracking of moving objects in unknown environments[J].CAAI Transactions on Intelligent Systems,2010,5():127.
[4]李 金,胡文广.基于颜色的快速人体跟踪及遮挡处理[J].智能系统学报,2010,5(4):353.
LI Jin,HU Wen-guang.Tracking fast movement using colors while accommodating occlusion[J].CAAI Transactions on Intelligent Systems,2010,5():353.
[5]刘侠,陶冶,邢春.统计差分与自启动的Camshift跟踪算法[J].智能系统学报,2011,6(4):355.
LIU Xia,TAO Ye,XING Chun.An objective tracking Camshift algorithm based onautomatic startup and the statistical differential method[J].CAAI Transactions on Intelligent Systems,2011,6():355.
[6]伍明,孙继银.基于粒子滤波的未知环境下机器人同时定位、地图构建与目标跟踪[J].智能系统学报,2013,8(2):168.[doi:10.3969/j.issn.1673-4785.201202001]
WU Ming,SUN Jiyin.Simultaneous localization, mapping and object tracking in an unknown environment using particle filtering[J].CAAI Transactions on Intelligent Systems,2013,8():168.[doi:10.3969/j.issn.1673-4785.201202001]
[7]贺超,刘华平,孙富春,等.采用Kinect的移动机器人目标跟踪与避障[J].智能系统学报,2013,8(5):426.[doi:10.3969/j.issn.1673-4785.201301028]
HE Chao,LIU Huaping,SUN Fuchun,et al.Target tracking and obstacle avoidance of mobile robot using Kinect[J].CAAI Transactions on Intelligent Systems,2013,8():426.[doi:10.3969/j.issn.1673-4785.201301028]
[8]王熙,吴为,钱沄涛.基于轨迹聚类的超市顾客运动跟踪[J].智能系统学报,2015,10(2):187.[doi:10.3969/j.issn.1673-4785.201401002]
WANG Xi,WU Wei,QIAN Yuntao.Trajectory clustering based customer movement tracking in a supermarket[J].CAAI Transactions on Intelligent Systems,2015,10():187.[doi:10.3969/j.issn.1673-4785.201401002]
[9]陈真,王钊.元认知粒子滤波目标跟踪算法[J].智能系统学报,2015,10(3):387.[doi:10.3969/j.issn.1673-4785.201405052]
CHEN Zhen,WANG Zhao.Object tracking algorithm with metacognitive model-based particle filters[J].CAAI Transactions on Intelligent Systems,2015,10():387.[doi:10.3969/j.issn.1673-4785.201405052]
[10]王杰,蒋明敏,花晓慧,等.基于投影直方图匹配的双目视觉跟踪算法[J].智能系统学报,2015,10(5):775.[doi:10.11992/tis.201410009]
WANG Jie,JIANG Mingmin,HUA Xiaohui,et al.Binocular object tracking method using projection histogram matching[J].CAAI Transactions on Intelligent Systems,2015,10():775.[doi:10.11992/tis.201410009]
[11]刘威,靳宝,周璇,等.基于特征融合及自适应模型更新的相关滤波目标跟踪算法[J].智能系统学报,2020,15(4):714.[doi:10.11992/tis.201803036]
LIU Wei,JIN Bao,ZHOU Xuan,et al.Correlation filter target tracking algorithm based on feature fusion and adaptive model updating[J].CAAI Transactions on Intelligent Systems,2020,15():714.[doi:10.11992/tis.201803036]
[12]林淑彬,吴贵山,姚文勇,等.基于光照自适应动态一致性的无人机目标跟踪[J].智能系统学报,2022,17(6):1093.[doi:10.11992/tis.202110023]
LIN Shubin,WU Guishan,YAO Wenyong,et al.Unmanned aerial vehicles object tracking based on illumination adaptive dynamic consistency[J].CAAI Transactions on Intelligent Systems,2022,17():1093.[doi:10.11992/tis.202110023]
备注/Memo
收稿日期:2010-05-21。
基金项目:江苏省高等学校自然科学研究面上项目(17KJB520039)；江苏省“333高层次人才培养工程科研项目”(BRA2018147)；江苏省高校“青蓝工程”项目(2020年)
作者简介:戴煜彤，硕士研究生，主要研究方向为人工智能与模式识别;陈志国，副教授，IEEE会员，主要研究方向为人工智能、计算机智能控制。参与申报国家科技支撑计划1项、863项目1项，参与教育部科学重大研究项目1项、973军工子项目1项，承担企业研究项目50余项，获得中国轻工业联合会科技进步奖二等奖1项、中国轻工业联合会科技进步奖三等奖1项、无锡市科技进步奖三等奖1项。发表学术论文20余篇;傅毅，副教授，主要研究方向为智能优化算法、生物信息。主持国家自然科学基金青年基金项目1项、江苏省自然科学基金项目1项，参与国家自然科学基金青年基金项目1项、江苏省环境监测科研基金项目1项。发表学术论文30余篇
通讯作者:陈志国.E-mail:427533@qq.com
更新日期/Last Update:
1900-01-01