Abstract
Attention mechanism in computer vision refers to scan, detect, and track a target object. This paper aims to develop and virtually train a machine learning model for object attention mechanism, combining object detection and mechanical automation. For this, we use Unity 3D Engine to model a simple scene in which two virtual cameras align together to realize a monocular attention in specific objects. Deep reinforcement learning, via ML-agent’s library, was used to train a model that aligns the virtual cameras. Moreover, the model was transferred to a physical camera to replicate the performance of attention mechanism.
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References
ml agents@unity3d.com: unity ml-agents toolkit (2022). https://github.com/Unity-Technologies/ml-agents/tree/develop/docs
Badue, C., et al.: Self-driving cars: a survey. Expert Syst. Appl. 165, 113816 (2021)
Baker, B., et al.: Emergent tool use from multi-agent autocurricula. arXiv preprint arXiv:1909.07528 (2019)
Grisetti, G., Kümmerle, R., Stachniss, C., Burgard, W.: A tutorial on graph-based slam. IEEE Intell. Transp. Syst. Mag. 2(4), 31–43 (2010)
Praeger, M., Xie, Y., Grant-Jacob, J.A., Eason, R.W., Mills, B.: Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments. Mach. Learn. Sci. Technol. 2(3), 035024 (2021)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
Technologies, U.: Monobehaviour.fixedupdate(). unity documentation (2021)
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7464–7475 (2023)
Ward, T.M., et al.: Computer vision in surgery. Surgery 169(5), 1253–1256 (2021)
Won, J., Gopinath, D., Hodgins, J.: Control strategies for physically simulated characters performing two-player competitive sports. ACM Trans. Graph. (TOG) 40(4), 1–11 (2021)
Zakka, K., Zeng, A., Lee, J., Song, S.: Form2Fit: learning shape priors for generalizable assembly from disassembly. In: Proceedings of the IEEE International Conference on Robotics and Automation (2020)
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Arizmendi, S., Paz, A., González, J., Ponce, H. (2024). Eye Control and Motion with Deep Reinforcement Learning: In Virtual and Physical Environments. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Computational Intelligence. MICAI 2023. Lecture Notes in Computer Science(), vol 14391. Springer, Cham. https://doi.org/10.1007/978-3-031-47765-2_8
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DOI: https://doi.org/10.1007/978-3-031-47765-2_8
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