Integrating Local Binary Patterns into Normalized Moment of Inertia for Updating Tracking Templates

SONG Huajun; XIAO Botao; HU Qinzhen; REN Peng

doi:10.1049/cje.2016.07.016

Volume 25 Issue 4

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Article Navigation > Chinese Journal of Electronics > 2016 > 25(4): 706-710

SONG Huajun, XIAO Botao, HU Qinzhen, et al., “Integrating Local Binary Patterns into Normalized Moment of Inertia for Updating Tracking Templates,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 706-710, 2016, doi: 10.1049/cje.2016.07.016

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SONG Huajun, XIAO Botao, HU Qinzhen, et al., “Integrating Local Binary Patterns into Normalized Moment of Inertia for Updating Tracking Templates,” Chinese Journal of Electronics, vol. 25, no. 4, pp. 706-710, 2016, doi: 10.1049/cje.2016.07.016

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Integrating Local Binary Patterns into Normalized Moment of Inertia for Updating Tracking Templates

doi: 10.1049/cje.2016.07.016

1.
College of Information and Control Engineering, China University of Petroleum(East China), Qingdao 266580, China;
2.
School of Electronic Engineering, Xidian University, Xi'an 710071, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61305012), China Scholarship Council, Shandong Outstanding Young Scientist Fund (No.BS2013DX006) and the Fundamental Research Funds for Central Universities (No.15CX05042A).

More Information

Corresponding author: REN Peng (corresponding author) received his Ph.D. degree in computer science from the University of York, UK. He is currently a professor with College of Information and Control Engineering, China University of Petroleum (East China). His research interests include machine learning and image analysis. (Email:pengren@upc.edu.cn)
Received Date: 2014-05-06
Rev Recd Date: 2015-07-06
Publish Date: 2016-07-10

Abstract

Abstract

This paper presents an efficient visual tracking framework which is robust to rotation, scale variation and occlusion. The target template is characterized by Local binary patterns (LBP), which exhibit invariance to rotation. The LBP features are then integrated into the Normalized moment of inertia (NMI) to decide whether the template requires update. This procedure enables an adaptive template matching strategy which addresses the tracking failures arising from scale variations. Kalman filtering is exploited for predicting the trajectory of the target when it is occluded. The matching efficiency is achieved by applying a locally pyramid searching scheme. Experimental results validate the efficiency and effectiveness of our tracking framework.
- Target tracking,
- Local binary patterns (LBP),
- Normalized moment of inertia (NMI),
- Kalman filtering

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References(12)

References

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