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auto-attentional mechanism in multi-domain convolutional neural networks for improving object tracking

International Journal of Intelligent Computing and Cybernetics 2022年第1期15卷 41-60页

作者： Jinchao Huang College of Mathematics and Information Engineering Longyan UniversityLongyanChina

Purpose-Multi-domain convolutional neural network(MDCNN)model has been widely used in object recognition and tracking in the field of computer ***,if the objects to be tracked move rapid or the appearances of moving objects vary dramatically,the conventional MDCNN model will suffer from the model drift *** solve such problem in tracking rapid objects under limiting environment for MDCNN model,this paper proposed an auto-attentional mechanism-based MDCNN(AA-MDCNN)model for the rapid moving and changing objects tracking under limiting ***/methodology/approach-First,to distinguish the foreground object between background and other similar objects,the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each ***,the bidirectional gated recurrent unit(Bi-GRU)architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature ***,the final feature map is obtained by fusion the above two feature maps for object *** addition,a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN ***-In order to validate the effectiveness and feasibility of the proposed AA-MDCNN model,this paper used ImageNet-Vid dataset to train the object tracking model,and the OTB-50 dataset is used to validate the AA-MDCNN tracking *** results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75%and success rate 2.41%,*** addition,the authors also selected six complex tracking scenarios in OTB-50 dataset;over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine *** addition,except for the scenario of multi-objects moving with each other,the proposed AA-MDCNN model solved the

关键词： Object tracking auto-attentional mechanism Multi-domain convolutional neural networks Bidirectional gated recurrent unit Composite loss function

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