A Feature-Enhanced Sparse Transformer Target Tracking Algorithm
Transformer's self-attention mechanism is computationally intensive and prone to be distracted by the background,resulting in insufficient capturing of effective information and lower tracking performance.To address the problem,a feature-enhanced Sparse Transformer target tracking algorithm is proposed.Feature extraction is performed based on Siamese network backbone.In feature enhancement module,the contextual information generated from multi-scale feature maps is utilized to enhance the local features.The most relevant features of Sparse Transformer are utilized to generate target focusing features,and position encoding is embedded to enhance the accuracy of tracking localization.The proposed tracking model is trained in an end-to-end manner,and extensive experiments are conducted on five datasets including OTB100,VOT2018,LaSOT,etc.The experimental results show that the proposed algorithm achieves better tracking performance and with a real-time tracking speed of 34 frames per second.
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