Abstract
Visual tracking is widely used in industrial systems such as vision servo systems and in intelligent robots. However, most tracking algorithms are designed without considering the balance of algorithmic efficiency and accuracy in system applications, making them less preferable for applications. This paper proposes a siamese global location-aware object tracking algorithm (SiamGLA) to address this issue. First, due to the limited performance of efficient lightweight backbone networks, this study designs an internal feature combination (IFC) module that improves feature representation with almost no additional parameters. Second, a global-aware (GA) attention module is proposed to improve the classification ability of foreground and background, which is especially important for trackers. Finally, a location-aware (LA) attention module is designed to improve the regression performance of the tracking framework. Comprehensive experiments show that SiamGLA is effective, and overcomes the drawbacks of poor robustness and weak generalization ability. When the performance reaches state-of-the-art, SiamGLA requires fewer calculations and parameters, making it more likely to be applied in practice.
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The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
This work was supported in part by the Beijing Natural Science Foundation under Grant L211017, and in part by the General Program of Beijing Municipal Education Commission under Grant KM202110005027, and in part by National Natural Science Foundation of China under Grant 61971016 and 61701011, and in part by Beijing Municipal Education Commission Cooperation Beijing Natural Science Foundation under Grant KZ201910005077.
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Li, J., Li, B., Ding, G. et al. Siamese global location-aware network for visual object tracking. Int. J. Mach. Learn. & Cyber. 14, 3607–3620 (2023). https://doi.org/10.1007/s13042-023-01853-2
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DOI: https://doi.org/10.1007/s13042-023-01853-2