Visual tracking based on adaptive patches appearance model

被引：0

作者：

Bao H. ^{[1
]}

Zhao Y.-Z. ^{[1
]}

Zhang C.-B. ^{[1
]}

Chen Z.-H. ^{[1
]}

机构：

[1] Department of Automation, University of Science and Technology of China, Hefei

来源：

Kongzhi yu Juece/Control and Decision | 2016年 / 31卷 / 03期

关键词：

Appearance changes; Local patches; Visual tracking;

D O I：

10.13195/j.kzyjc.2015.0035

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

For the tracking problem when the target undergoes rapid and significant appearance changes, a novel tracking algorithm is presented. The object's appearance is represented by a set of local patches with inherent spatial geometric constraints relationship. It probabilistically adapts to the object's appearance changes by removing and adding the local patches. The locations of new patches are determined by the global color property, which can improve the limitations of the traditional patch-based algorithms that the appearance model can't be updated in time during tracking. Experimental results show that the proposed algorithm performs in many cases with high adaptivity to appearance changes, which has high accuracy to objects with drastically changes. © 2016, Editorial Office of Control and Decision. All right reserved.

引用

页码：448 / 452

页数：4

共 18 条

[1]

Perez P., Hue C., Vermaak J., Et al., Color-based probabilistic tracking, Proc of the 7th European Conf on Computer Vision, pp. 661-675, (2002)

[2]

Wu Y., Lim J., Yang M.H., Online object tracking: A benchmark, Proc of IEEE Conf on Computer Vision and Pattern Recognition, pp. 2411-2418, (2013)

[3]

Comaniciu D., Ramesh V., Meer P., Kernel-based object tracking, IEEE Trans on Pattern Analysis and Machine Intelligence, 25, 5, pp. 564-577, (2003)

[4]

Fieguth P., Terzopoulos D., Color-based tracking of heads and other mobile objects at video frame rates, Proc of IEEE Conf on Computer Vision and Pattern Recognition, pp. 21-27, (1997)

[5]

Collins R.T., Liu Y., Leordeanu M., Online selection of discriminative tracking features, IEEE Trans on Pattern Analysis and Machine Intelligence, 27, 10, pp. 1631-1643, (2005)

[6]

Ross D.A., Lim J., Lin R.S., Et al., Incremental learning for robust visual tracking, Int J of Computer Vision, 77, 1-3, pp. 125-141, (2008)

[7]

Han B., Davis L., On-line density-based appearance modeling for object tracking, Proc of IEEE Int Conf on Computer Vision, pp. 1492-1499, (2005)

[8]

Zhu M.Q., Wang Z.L., Chen Z.H., Human visual intelligence and particle filter based robust object tracking algorithm, Control and Decision, 27, 11, pp. 1720-1724, (2012)

[9]

Li W.W., Zhang C.B., Chen Z.H., Et al., Particle filter tracking based on feature-learning and feature- memory template update mechanism, J of University of Science and Technology of China, 44, 4, pp. 292-302, (2014)

[10]

Adam A., Rivlin E., Shimshoni I., Robust fragments-based tracking using the integral histogram, Proc of IEEE Conf on Computer Vision and Pattern Recognition, pp. 798-805, (2006)

← 1 2 →