In order to solve the problem of modal heterogeneity between visible light images and thermal in-frared images due to different imaging principles,an asymmetric hierarchical feature fusion RGBT tracking network is proposed.Firstly,a two-stream network is used to extract visual light and thermal infrared fea-tures;then through the modal feature extraction module to mine different modal features and adaptively ag-gregate the obtained features to obtain features that are conducive to enhancing the visible light mode;fi-nally,the aggregated features obtained by each layer and the visible light features obtained by the two-stream network perform enhanced fusion to obtain more robust features.Experimental results on GTOT,RGBT234 and LasHeR datasets show that the tracking precision rate(PR)and success rate(SR)of the network reach 92.2％/77.2％,82.9％/61.1％and 52.7％/40.3％,compared with the current mainstream RGBT target tracking network,both PR and SR have been improved,which verifies the effectiveness of the network.
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