Visual Tracking Based on Adaptive Mean Shift Multiple Appearance Models

被引：2

作者：

Dhassi Y. ^{[1
]}

Aarab A. ^{[1
]}

机构：

[1] Laboratory of Electronics, Signals, Systems and Computers, Department of Physics Faculty of Sciences Dhar-Mahraz, Sidi Mohamed Ben Abdellah University, Fes, Rabat

来源：

Pattern Recognition and Image Analysis | 2018年 / 28卷 / 03期

关键词：

interactive multiple models; mean shift; visual tracking;

D O I：

10.1134/S1054661818030057

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To overcome the tracking issues caused by the complex environment namely, illumination variation and background clutters, tracking algorithm was proposed based on multi-cues fusion to construct a robust appearance model, indeed the global motion is estimated using the H∞ filter based on the nearly constant velocity motion model, then the traditional Mean Shift (MS) estimate the local state associated with each sub appearance model, finally the weights of the sub appearance models are adjusted and combined to estimate the final state. The proposed method is tested on public videos that present different environment issues. Experiences and comparisons conducted show the robustness of our methods in challenging tracking conditions. © 2018, Pleiades Publishing, Ltd.

引用

页码：439 / 449

页数：10

共 23 条

[1]

Li X., Hu W., Shen C., Zhang Z., Dick A., van den Hengel A., A survey of appearance models in visual object tracking, ACM Trans. Intellig. Syst. Technol., 4, pp. 1-48, (2013)

[2]

Wu S., Hong L., Hand tracking in a natural conversational environment by the interacting multiple model and probabilistic data association (IMM-PDA) algorithm, Pattern Recogn., 38, pp. 2143-2158, (2005)

[3]

Zhang L., Zhang D., Wu C., Robust mean-shift tracking with corrected background weighted histogram, IET Computer Vision, 6, 1, pp. 62-69, (2012)

[4]

Int. J. Imag. Robotic, 17, 1, (2017)

[5]

Comaniciu D., Ramesh V., Meer P., Kernelbased object tracking, IEEE Trans. Pattern Anal. Mach. Intell., 25, 5, pp. 564-577, (2003)

[6]

Arulampalam M., Maskell S., Gordon N., Clapp T., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Trans. Signal Process., 50, 2, pp. 174-189, (2002)

[7]

Dou J., Li J., Robust visual tracking based on interactive multiple model particle filter by integrating multiple cues, Neurocomputing, 135, pp. 118-129, (2014)

[8]

Jepson A.D., Fleet D.J., El-Maraghi T.F., Robust online appearance models for visual tracking, Proc. CVPR, (2001)

[9]

Collins R.T., Liu Y., Leordeanu M., Online selection of discriminative tracking features, IEEE Trans. Pattern Anal. Mach. Intell., 27, 10, pp. 1631-1643, (2004)

[10]

Li G., Liang D., Huang Q., Jiang S., Gao W., Object tracking using incremental 2d-lda learning and Bayes inference, Proc. ICIP, (2008)

← 1 2 3 →