Adaptive Incremental Principal Component Analysis Visual Tracking Method Based on Temporal Characteristics

Cai Zi-xing; Peng Meng; Yu Ling-li

doi:10.11999/JEIT141646

Volume 37 Issue 11

Nov. 2015

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Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(11): 2571-2577

Liu Yan, Lin Yun, Tan Wei-xian, Hong Wen. DEM Extraction Based on Interferometric Circular SAR[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1463-1469. doi: 10.11999/JEIT141022

Citation:

Cai Zi-xing, Peng Meng, Yu Ling-li. Adaptive Incremental Principal Component Analysis Visual Tracking Method Based on Temporal Characteristics[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2571-2577. doi: 10.11999/JEIT141646

Liu Yan, Lin Yun, Tan Wei-xian, Hong Wen. DEM Extraction Based on Interferometric Circular SAR[J]. Journal of Electronics & Information Technology, 2015, 37(6): 1463-1469. doi: 10.11999/JEIT141022

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Adaptive Incremental Principal Component Analysis Visual Tracking Method Based on Temporal Characteristics

doi: 10.11999/JEIT141646

Funds:

The Major Research Project of the National Natural Science Foundation of China (90820302)

Received Date: 2014-12-25
Rev Recd Date: 2015-07-20
Publish Date: 2015-11-19

Abstract

Abstract

Existing visual tracking methods based on incremental Principal Component Analysis (PCA) learning have two problems. First, the measurement model does not consider the continuation characteristics of the object appearance changes. Second, when the manifold distribution of target appearance is non-linear structure, the incremental principal component analysis learning based on fixed update frequency can not adapt to changes of subspace model. Therefore, the more reasonable a priori probability distribution of targets is proposed based on the continuity of the object appearance changes in the subspace model. Then, according to the matching degree between the current tracking results and the subspace model, the proposed method adaptively adjusts forgetting factor, in order to make the subspace model more adaptable to the object appearance change. Experimental results show that the proposed method can improve the tracking accuracy and robustness.
- Visual tracking,
- Principal Component Analysis (PCA),
- Incremental subspace learning,
- Forgetting factor,
- Adaptive increment

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References(22)

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