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.
Recommended Citation
Zhou, Wei; Liu, Yuxiang; Liao, Guangping; and Ma, Xin
(2022)
"Siamese Object Tracking Algorithm Combined with the Intersection over Union Loss,"
Journal of System Simulation: Vol. 34:
Iss.
9, Article 5.
DOI: 10.16182/j.issn1004731x.joss.21-0377
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss9/5
First Page
1956
Revised Date
2021-07-08
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-0377
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.
DOI
10.16182/j.issn1004731x.joss.21-0377
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