| Summary: | RGBT target tracking uses the advantages of two different modes of visible light(RGB) and thermal infrared(T) to solve the common modal limitation problem in single mode target tracking,so as to improve the performance of target tracking in complex environment.In the RGBT object tracking algorithm,the precise location of the object and the effective fusion of the two modalities are very important issues.In order to accurately locate the object and effectively fuse the two modalities,this paper proposes a new method to explore high-rank feature maps and introduce position attention for RGBT object tracking.The method first uses location attention to focus on the location information of the object according to the deep and shallow features of the backbone network,and then focuses on the importance of the features by exploring the high-rank feature maps before the fusion of the two modalities to guide the modal features fusion.In order to focus on the object location information,this paper uses the average pooling operation on the rows and columns.For the high-rank feature guidance module,this paper guides the fusion of feature maps according to the rank of the feature maps.In order to remove redundancy and noise and achieve more robust feature expression,the feature graph with small rank is deleted directly.Experimental results on two RGBT tracking benchmark data sets show that compared with other RGBT target tracking methods,the proposed method achieves better tracking results in accuracy and success rate.
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