首页 > 论文 > 光学学报 > 38卷 > 5期(pp:515003--1)

基于分类-验证模型的视觉跟踪算法研究

Visual Tracking Algorithm Based on Classification-Validation Model

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

针对相似度目标跟踪算法主要考虑目标的类内相似,而忽略不同目标的类间差异的问题,提出基于分类-验证模型的视觉跟踪算法。该算法通过增加目标的属性(类别)信息,利用相似度信息与类别信息构建损失函数,在高维空间学习目标的类内相似和类间差异;将目标模板与候选目标输入网络模型,分别通过分类与验证模块实现网络参数更新;利用训练网络提取目标模板与候选目标的深度嵌入特征,实现目标跟踪。在OTB50和UAV123数据库上进行实验,结果表明,该算法可以大幅提高跟踪效果,对相似目标具有较强的稳健性。

Abstract

In order to solve the problems that the similarity target tracking algorithm mainly considers the intraclass similarity of targets and ignores the interclass differences of different targets. A visual tracking algorithm based on classification-validation model is proposed, which adds attribute information to the similarity algorithm. The proposed algorithm constructs the loss function with similarity and class information, and learns intraclass similarity and interclass differences in high dimensional space. The classification and verification module is adopted to update network parameters when the target template and candidate target input into the network model. With the trained network, the deep embedding feature of target and candidate target is extracted, thus, the target tracking is achieved. Experiments are carried out on the OTB50 and UAV123 databases. Results show that the proposed algorithm can improve the tracking effect with increased target information, and has strong robustness to the similar targets.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TN919.82

DOI:10.3788/aos201838.0515003

所属栏目:机器视觉

基金项目:国家自然科学基金(61472442,61773397,61701524)、陕西省科技新星资助项目(2015kjxx-46)

收稿日期:2017-11-06

修改稿日期:2017-12-12

网络出版日期:--

作者单位    点击查看

吴敏:中国人民解放军空军工程大学航空航天工程学院, 陕西 西安 710038
查宇飞:中国人民解放军空军工程大学航空航天工程学院, 陕西 西安 710038
张园强:中国人民解放军空军工程大学航空航天工程学院, 陕西 西安 710038
库涛:中国人民解放军空军工程大学航空航天工程学院, 陕西 西安 710038
李运强:中国人民解放军空军工程大学航空航天工程学院, 陕西 西安 710038
张胜杰:中国人民解放军空军工程大学航空航天工程学院, 陕西 西安 710038

联系人作者:查宇飞(463431261@qq.com)

备注:吴敏(1994-),男,硕士研究生,主要从事计算机视觉、图像处理与分析方面的研究。E-mail: kj123123213@163.com

【1】Ojala T, Pietikinen M, Menp T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.

【2】Zhao G P, Shen Y P, Wang J Y. Adaptive feature fusion object tracking based on circulant structure with kernel[J]. Acta Optica Sinica, 2017, 37(8): 0815001.
赵高鹏, 沈玉鹏, 王建宇. 基于核循环结构的自适应特征融合目标跟踪[J]. 光学学报, 2017, 37(8): 0815001.

【3】Li S S, Zhao G P, Wang J Y. Distractor-aware object tracking based on multi-feature fusion and scale-adaption[J]. Acta Optica Sinica, 2017, 37(5): 0515005.
李双双, 赵高鹏, 王建宇. 基于特征融合和尺度自适应的干扰感知目标跟踪[J]. 光学学报, 2017, 37(5): 0515005.

【4】Held D, Thrun S, Savarese S. Learning to track at 100 fps with deep regression networks[C]. European Conference on Computer Vision (ECCV), 2016: 749-765.

【5】Chen K, Tao W B. Once for all: a two-flow convolutional neural network for visual tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 99: 1-15.

【6】Tao R, Gavves E, Smeulders A W M. Siamese instance search for tracking[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 1420-1429.

【7】Bertinetto L, Valmadre J, Henriques J F, et al. Fully-convolutional Siamese networks for object tracking[C]. European Conference on Computer Vision (ECCV), 2016: 850-865.

【8】He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770-778.

【9】Zeng X, Ouyang W, Yang B, et al. Gated bi-directional CNN for object detection[C]. European Conference on Computer Vision (ECCV), 2016: 354-369.

【10】Ma C, Huang J B, Yang X, et al. Hierarchical convolutional features for visual tracking[C]. IEEE International Conference on Computer Vision (ICCV), 2015: 3074-3082.

【11】Leal-Taixe L, Canton-Ferrer C, Schindler K. Learning by tracking: siamese CNN for robust target association[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016: 418-425.

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

【13】Hong Z, Chen Z, Wang C, et al. Multi-store tracker (muster): a cognitive psychology inspired approach to object tracking[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 749-758.

【14】Danelljan M, Robinson A, Khan F S, et al. Beyond correlation filters: learning continuous convolution operators for visual tracking[C]∥European Conference on Computer Vision (ECCV), 2016: 472-488.

【15】Wang N Y, Li S Y, Gupta A, et al. Transferring rich feature hierarchies for robust visual tracking[J]. Computer Science, 2015.

引用该论文

Wu Min,Zha Yufei,Zhang Yuanqiang,Ku Tao,Li Yunqiang,Zhang Shengjie. Visual Tracking Algorithm Based on Classification-Validation Model[J]. Acta Optica Sinica, 2018, 38(5): 0515003

吴敏,查宇飞,张园强,库涛,李运强,张胜杰. 基于分类-验证模型的视觉跟踪算法研究[J]. 光学学报, 2018, 38(5): 0515003

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF