Online Object Tracking Based on Gray-level Co-occurrence Matrix and Third-order Tensor

JIN Guangzhi; SHI Linsuo; CUI Zhigao; LIU Hao; MU Weijie

doi:10.11999/JEIT151108

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1609-1615

JIN Guangzhi, SHI Linsuo, CUI Zhigao, LIU Hao, MU Weijie. Online Object Tracking Based on Gray-level Co-occurrence Matrix and Third-order Tensor[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1609-1615. doi: 10.11999/JEIT151108

Citation:

JIN Guangzhi, SHI Linsuo, CUI Zhigao, LIU Hao, MU Weijie. Online Object Tracking Based on Gray-level Co-occurrence Matrix and Third-order Tensor[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1609-1615. doi: 10.11999/JEIT151108

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Online Object Tracking Based on Gray-level Co-occurrence Matrix and Third-order Tensor

doi: 10.11999/JEIT151108

Funds:

The National Natural Science Foundation of China (61501470)

Received Date: 2015-09-29
Rev Recd Date: 2016-03-03
Publish Date: 2016-07-19

Abstract

Abstract

In order to improve the stability and accuracy of the object tracking under different conditions, an online object tracking algorithm based on Gray-Level Co-occurrence Matrix (GLCM) and third-order tensor is proposed. First, the algorithm extracts the gray-level information of target area to describe the two high discrimination features of target by GLCM, the dynamic information about target changing is fused by third-order tensor theory, and the third-order tensor appearance model of the object is constructed. Then, it uses bilinear space theory to expand the appearance model, and implements the incremental learning. Updating of model by online models characteristic value description, thus computation of the model updating is greatly reduced. Meanwhile, the static observation model and adaptive observation model are constructed, and secondary combined stable tracking of object is achieved by dynamic matching of two observation models. Experimental results indicate that the proposed algorithm can effectively deal with the moving object tracking on a variety of challenging scenes, and the average tracking error is less than 9 pixels.
- Object tracking,
- Gray-Level Co-occurrence Matrix (GLCM),
- Third-order tensor,
- Linear space,
- Online tracking

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References(20)

References

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