Object Tracking Method Based on Sparse Optimization of Local Sensing

LIU Daqian; LIU Wanjun; FEI Bowen

doi:10.11999/JEIT170473

Volume 40 Issue 2

Feb. 2018

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(2): 272-281

Meng ZHANG, Jingwei ZHANG, Guoqing LI, Ruixia WU, Xiaoyang ZENG. Efficient Hardware Optimization Strategies for Deep Neural Networks Acceleration Chip[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1510-1517. doi: 10.11999/JEIT210002

Citation:

LIU Daqian, LIU Wanjun, FEI Bowen. Object Tracking Method Based on Sparse Optimization of Local Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(2): 272-281. doi: 10.11999/JEIT170473

Citation:

PDF( 20184 KB)

Object Tracking Method Based on Sparse Optimization of Local Sensing

doi: 10.11999/JEIT170473

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)

Received Date: 2017-05-17
Rev Recd Date: 2017-08-01
Publish Date: 2018-02-19

Abstract

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

FullText(HTML)

References(21)

References

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.

Relative Articles

Supplements(0)

Cited By