Research on Target Tracking Algorithm of Twin Networks Integrating Attention Mechanism


  • Relizha Yeerlanbieke
  • Huazhang Wang



Deep learning, Twin neural network, Target tracking, Attention mechanism


Aiming at the current stage of the twin network target tracking algorithm, the tracking target is occluded, the tracking is affected by illumination, and the target's scale change from far to near or from near to far causes tracking failure. This article will optimize and improve from two directions. The twin neural network first uses an adaptive detailed feature extraction, adds a residual network to the twin network, and embeds a detailed feature retention module in each layer, amplifies the changes in the target feature, and retains the important structure of the original target feature Details: Secondly, the introduction of a spatial attention mechanism allows the main branch to pay more attention to the area to be matched, improves the ability to distinguish features, and makes the tracking effect better. In order to verify the effectiveness of this experiment, this experiment was tested on the data set OTB2015. The experiment proved that the proposed algorithm performs better in accuracy and success rate.


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16 December 2021

How to Cite

Yeerlanbieke, R., & Wang, H. (2021). Research on Target Tracking Algorithm of Twin Networks Integrating Attention Mechanism. Frontiers in Business, Economics and Management, 2(3), 78–81.