Ms. Xian Wang Profile

Xian Wang

at Anhui Univ

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Publications (2)

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Proceedings Article | 27 January 2021 Paper

Robust kernel correlation filter tracker based on adaptive scale and occlusion detection

Xuewei Fang, Huabin Wang, Liang Tao, Xian Wang

Proceedings Volume 11720, 117200H (2021) https://doi.org/10.1117/12.2589374

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Correlation filter-based trackers exploit large numbers of cyclically shifted samples to train the object classifier, which can achieve good results in tracking accuracy and speed. However, when in complex scenes such as occlusion or deformation, tracking drift or loss will occur. In this paper, a kernel correlation filter tracker base on scale adaptive and occlusion detection is proposed to strengthen the tracker robustness. Firstly, a robust appearance model combine the gradient feature and color feature is proposed to enhance the features representation ability; Secondly, a scale adaptive mechanism is introduced to handle the problem of the fixed template size, and the Newton method is used to find the maximum response value to more accurate predict the center position of the target and estimate the target scale; Finally, the occlusion detection scheme adopted when update model to avoid tracking failure due to appearance model pollution. Experiments are performed on the OTB2013 Benchmark Dataset, the results show that, compared to the basic tracker, we obtain an absolute gain of 6.6% and 13.4% respectively in mean distance precision and mean overlap precision.

Proceedings Article | 14 August 2019 Paper

Novel algorithm for finger vein recognition based on inception-resnet module

Xian Wang, Huabin Wang, Ying He, Yijun Ding, Liang Tao

Proceedings Volume 11179, 111791D (2019) https://doi.org/10.1117/12.2539624

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The finger vein feature extraction algorithm based on global or local features is sensitive to rotation, translation and scaling. Convolutional neural networks have higher robustness, but fewer finger vein samples are prone to over-fitting. Therefore, this paper designs a network architecture FingerveinNet for finger vein recognition. Firstly, based the Inception-resnet^[1] module, the design of the finger vein network architecture is used to extract the multi-scale finger vein features while slowing down the gradient disappearance problem without increasing the parameters. Secondly, the center-loss is used as the loss function to optimize the network model and improve. The discriminability of feature vectors for better detail discrimination. Experiments on three international finger vein databases FV-TJ, FV-USM and PolyU show that the proposed method is robust to rotation and translation, and the effectiveness of the proposed method is verified.

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