SONG Huajun, XIAO Botao, HU Qinzhen, REN Peng. Integrating Local Binary Patterns into Normalized Moment of Inertia for Updating Tracking Templates[J]. Chinese Journal of Electronics, 2016, 25(4): 706-710. doi: 10.1049/cje.2016.07.016
Citation: SONG Huajun, XIAO Botao, HU Qinzhen, REN Peng. Integrating Local Binary Patterns into Normalized Moment of Inertia for Updating Tracking Templates[J]. Chinese Journal of Electronics, 2016, 25(4): 706-710. doi: 10.1049/cje.2016.07.016

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

doi: 10.1049/cje.2016.07.016
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).
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  • 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
  • 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.
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