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Journal of System Simulation

Abstract

Abstract: To improve the accuracy of the object bounding box regression prediction of the SiamRPN,solve the problem of low discrimination of positive samples in classification prediction and the lack of correlation between regression prediction and classification prediction, an improved object tracking algorithm of SiamRPN which combined with IoU(intersection over union) loss is proposed. A joint optimization module of IoU-smooth L1 is designed to optimize the IoU loss of the best positive sample and the smooth L1 loss of other positive samples jointly. According to the regression prediction results, the weighted classification prediction is performed on the positive samples with the weight calculated by the IoU of the prediction box and the truth box, so as to increase the discrimination between the positive samples, while ensuring the correlation between the classification prediction and the regression prediction.The results show that the proposed algorithm can effectively improve the tracking performance.

First Page

1956

Revised Date

2021-07-08

Last Page

1967

CLC

TP391.4

Recommended Citation

Wei Zhou, Yuxiang Liu, Guangping Liao, Xin Ma. Siamese Object Tracking Algorithm Combined with the Intersection over Union Loss[J]. Journal of System Simulation, 2022, 34(09): 1956-1967.

Corresponding Author

Yuxiang Liu,494172184@qq.com

DOI

10.16182/j.issn1004731x.joss.21-0377

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