Abstract: When Transformer is used alone for feature extraction in object tracking,the absence of inductive bias makes it difficult to adapt to change in target scale and appearance.To address this,this paper introduces target tracking method based on cross scale fusion of features and trajectory prompts(Cross Scale Fusion of features and Trajectory Prompts Tracker CSFTP-Tracker).In constructing the input for the object tracking network,both the template image and the search image are simultaneously fed into an encoder that fuses CNN and ViT.A key design element is the multi-level spatial-aware pyramid module (Multi-Level Spatial Awareness Pyramid,MSAP).Firstly,the multi-scale CNN features are enhanced with self-attention to strengthen target location information.These multi-scale features are then fused with the F-embeddings features from the ViT and input into the ViT encoder.This fusion strategy not only enhances information interaction between patches within the ViT but also enables the network to leverage both the local features of CNN and the global dependency capabilities of the Transformer.Furthermore,the fused features extracted by the ViT,along with the trajectory prompt features,are fed into the decoder,where autoregressive learning is employed to predict the target's position.Experimental results on the GOT-10k dataset show that,compared to the baseline models,the proposed network improves the average overlap(AO) by 1.3% and increases the success rate score at a 0.5 threshold(SR0.5) by 1.4%.

Key words: Transformer,Object tracking,Inductive bias,Encoder,Trajectory prompt