局部感知下的稀疏优化目标跟踪方法

刘大千; 刘万军; 费博雯

doi:10.11999/JEIT170473

局部感知下的稀疏优化目标跟踪方法

doi: 10.11999/JEIT170473

1.
(辽宁工程技术大学电子与信息工程学院葫芦岛 125105)
2.
(辽宁工程技术大学软件学院葫芦岛 125105)
3.
(辽宁工程技术大学工商管理学院葫芦岛 125105)

基金项目:

国家自然科学基金(61172144)，辽宁省科技攻关计划项目(2012216026)

计量
- 文章访问数: 1442
- HTML全文浏览量: 127
- PDF下载量: 280
- 被引次数: 0
出版历程
- 收稿日期: 2017-05-17
- 修回日期: 2017-08-01
- 刊出日期: 2018-02-19

Object Tracking Method Based on Sparse Optimization of Local Sensing

1.
(School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China)
2.
(School of Software, Liaoning Technical University, Huludao 125105,China)
3.
(School of Business and Management, Liaoning Technical University, Huludao 125105, China)

Funds:

The National Natural Science Foundation of China (61172144), The Science and Technology Foundation of Liaoning Province (2012216026)

摘要

摘要: 针对传统稀疏表示跟踪算法在复杂背景中易出现跟踪漂移问题，该文提出一种局部感知下的稀疏优化目标跟踪方法。首先，将首帧确定的目标区域进行非重叠均匀分割，并利用目标的全局特征和局部特征联合建模。然后，提出一种局部感知校验方法约束稀疏优化匹配过程，从而确定最优匹配样本。最后，在模板更新中提出一种决策方法对遮挡进行检测，并针对不同遮挡情况采取相应的更新策略，使得更新后的模板集更加完善。实验在10个标准库视频序列中测试，并与目前较流行的目标跟踪算法在跟踪效果、成功率等方面进行比较，实验结果表明，提出的跟踪方法在局部遮挡、目标形变、复杂背景等条件下跟踪准确、适应性强。
- 目标跟踪 /
- 局部感知 /
- 稀疏优化 /
- 遮挡决策
Abstract: The problem of tracking drift is produced easily by traditional sparse representation tracking methods in complex scene. To solve this problem, a novel tracking approach based on sparse optimization of local sensing is proposed. Firstly, the object area of the first frame is divided into non-overlapping uniform segmentation, and building the template set using global features and local features. Then, a local sensing correction method for constraining sparse optimization matching process is utilized to determine the optimal matching samples. Finally, a new method of occlusion decision is used to detect occlusion, and updating strategies are adopted according to different occlusion conditions, which makes the template sets more complete in the process of template update. The experiments compare with state-of-the-art tracking algorithms on 10 tracking test sequences of benchmark library. Experiment results indicate that the proposed method possesses characteristics of accurate tracking and strong adaptability in the conditions of partial occlusion, deformation, and complex background.
- Object tracking /
- Local sensing /
- Sparse optimization /
- Occlusion decision

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参考文献(21)

QI Yuanchen WU Chengdong CHEN Dongyue, et al. Superpixel tracking based on sparse representation[J]. Journal of Electronics Information Technology, 2015, 37(3): 529-535. doi: 10.11999/JEIT140374.

齐苑辰, 吴成东, 陈东岳, 等. 基于稀疏表达的超像素跟踪算法[J]. 电子与信息学报, 2015, 37(3): 529-535. doi: 10.11999/ JEIT140374.

李文娟,顾红,苏卫民. 基于多伯努利概率假设密度的扩展目标跟踪方法[J]. 电子与信息学报, 2016, 38(12): 3114-3121.doi: 10.11999/JEIT160372.

LI Wenjuan, GU Hong, and SU Weimin. Extended target tracking method based on multi-bernoulli probability hypothesis density[J]. Journal of Electronics Information Technology, 2016, 38(12): 3114-3121. doi: 10.11999/JEIT 160372.

杨峰, 张婉莹. 一种多模型贝努利粒子滤波机动目标跟踪算法[J]. 电子与信息学报, 2017 39(3): 634-639. doi: 10.11999 /JEIT160467.

YANG Feng and ZHANG Wanying. Multiple model Bernoulli particle filter for maneuvering target tracking[J]. Journal of Electronics Information Technology, 2017, 39(3): 634-639. doi: 10.11999/JEIT160467.

MEI Xue and LING Haibin. Robust visual tracking using L1 minimization[C]. IEEE 12th International Conference on Computer Vision, Florence, Italy, 2009: 1436-1443. doi: 10.1109/ICCV.2009.5459292.

BAO Chenglong, WU Yi, LING Haibin, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012: 1830-1837. doi: 10.1109/CVPR.2012.6247881.

ZHANG Tianzhu, Ghanem B, LIU Si, et al. Robust visual tracking via multi-task sparse learning[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012: 2042-2049. doi: 10. 1109/CVPR.2012.6247908.

张旭东, 陈仲海, 胡良梅, 等. 基于联合模板稀疏表示的目标跟踪方法[J]. 控制与决策, 2015, 30(9): 1696-1700. doi: 10.13195/j.kzyjc.2014.1175.

ZHANG Xudong, CHEN Zhonghai, HU Liangmei, et al. Object tracking method based on sparse representation of joint template[J]. Control and Decision, 2015, 30(9): 1696-1700. doi: 10.13195/j.kzyjc.2014.1175.

ZHANG Shengping, ZHOU Huiyu, JIANG Feng, et al. Robust visual tracking using structurally random projection and weighted least squares[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(11): 1749-1760. doi: 10.1109/TCSVT.2015.2406194.

胡秀华, 郭雷, 李晖晖, 等. 一种结合空间信息和稀疏字典优化的目标跟踪算法[J]. 控制与决策, 2016, 31(12): 2170-2176. doi: 10.13195/j.kzyjc.2015.1489.

HU Xiuhua, GUO Lei, LI Huihui, et al. An object tracking algorithm combining spatial information and sparse dictionary optimization[J]. Control and Decision, 2016, 31(12): 2170-2176. doi: 10.13195/j.kzyjc.2015.1489.

MEI X, LING Haibin, WU Yi, et al. Efficient minimum error bounded particle resampling L1 tracker with occlusion detection[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2661-2675. doi: 10.1109/TIP.2013.2255301.

WU Yi, LIM Jongwoo, and YANG Minghsuan. Online object tracking: A benchmark[C]. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 2411-2418. doi: 10.1109/ CVPR.2013.312.

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.

ORON S, HILLEL A, and LEVI D. Locally orderless tracking [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012: 1940-1947. doi: 10.1109/CVPR.2012.6247895.

KWON J and LEE K M. Visual tracking decomposition [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010: 1269-1276. doi: 10.1109/CVPR.2010.5539821.

ZHONG Wei, LU Huchuan, and YANG Minghsuan. Robust object tracking via sparsity-based collaborative model[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA, 2012: 1838-1845. doi: 10.1109/CVPR.2012.6247882.

ADAM A, RIVLIN E, and SHIMSHONI I. Robust fragments-based tracking using the integral histogram[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, NY, USA, 2006: 798-805. doi: 10.1109/CVPR.2006.256.

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