Tracking Computer Vision Algorithm Based on Fusion Twin Network

Deep learning technology has promoted the rapid development of visual object tracking, among which algorithms based on twin networks are a hot research direction. Although this method has broad application prospects, its performance is often greatly reduced when encountering target occlusion or similar objects in the background. In response to this issue, a method is proposed to integrate channel and spatial dimension attention mechanisms into the backbone architecture of twin networks, to optimize the algorithm's recognition accuracy for tracking targets and its stability in changing environments. Then, a region recommendation network based on adaptive anchor box generation is adopted, combined with twin networks to enhance the network's modeling ability for complex situations. Finally, a new visual tracking algorithm is designed. Through comparative experiments, the success rate of the former increased by 0.6% and 0.9% respectively on the two datasets, and its accuracy also increased by 1.2% and 1.8% accordingly. The success rate of the latter increased by 1.5% and 1.2% respectively in the two datasets, and the accuracy also increased by 1.2% and 0.6% respectively. From this, the improved algorithm can improve the performance of target tracking and has certain application potential in visual target tracking.

@article{Wang2024,
  title     = {Tracking Computer Vision Algorithm Based on Fusion Twin Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xin Wang},
  doi       = {10.14569/IJACSA.2024.0151093},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151093}
}