Abstract
In recent years, the study of Unmanned Aerial Vehicle (UAV) autonomous landing has been a hot research topic. Aiming at UAV’s landmark localization, the computer vision algorithms have excellent performance. In the computer vision research field, the deep learning methods are widely employed in object detection and localization. However, these methods rely heavily on the size and quality of the training datasets. In this paper, we propose to exploit the Landmark-Localization Network (LLNet) to solve the UAV landmark localization problem in terms of a deep reinforcement learning strategy with small-sized training datasets. The LLNet learns how to transform the bounding box into the correct position through a sequence of actions. To train a robust landmark localization model, we combine the policy gradient method in deep reinforcement learning algorithm and the supervised learning algorithm together in the training stage. The experimental results show that the LLNet is able to locate the landmark precisely.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Luo, C., Yu, L., Ren, P.: A vision-aided approach to perching a bio-inspired unmanned aerial vehicle. IEEE Trans. Ind. Electron. 65(5), 3976–3984 (2018)
Yu, L., et al.: Deep learning for vision-based micro aerial vehicle autonomous landing. Int. J. Micro Air Veh. (2018)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Hong, S., You, T., Kwak, S., Han, B.: Online tracking by learning discriminative saliency map with convolutional neural network. In: International Conference on Machine Learning, pp. 597–606 (2015)
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Computer Vision and Pattern Recognition, pp. 4293–4302 (2016)
Wang, N., Li, S., Gupta, A., Yeung, D.-Y.: Transferring rich feature hierarchies for robust visual tracking (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014)
Li, H., Li, Y., Porikli, F.: Robust online visual tracking with a single convolutional neural network. In: Cremers, D., Reid, I., Saito, H., Yang, M.H. (eds.) ACCV 2014. LNCS, vol. 9007, pp. 194–209. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16814-2_13
Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: International Conference on Computer Vision, pp. 3119–3127 (2015)
Yun, S., Choi, J., Yoo, Y., Yun, K., Choi, J.Y.: Action-decision networks for visual tracking with deep reinforcement learning (2017)
Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning. MIT Press, Cambridge (1998)
Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
Sammut, C., Webb, G.I.: Encyclopedia of Machine Learning And Data Mining. Springer, Boston (2017)
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets (2014)
Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. In: Sutton, R.S. (ed.) Reinforcement Learning, pp. 5–32. Springer, Boston (1992). https://doi.org/10.1007/978-1-4615-3618-5_2
Kristan, M., et al.: The visual object tracking VOT2015 challenge results. In: International Conference on Computer Vision Workshops, pp. 1–23 (2015)
Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.-H.: Fast visual tracking via dense spatio-temporal context learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 127–141. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_9
Choi, J., Chang, H.J., Jeong, J., et al.: Visual tracking using attention-modulated disintegration and integration. In: Computer Vision and Pattern Recognition, pp. 4321–4330 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, X., Ren, P., Yu, L., Han, L., Deng, X. (2018). UAV First View Landmark Localization via Deep Reinforcement Learning. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-97785-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97784-3
Online ISBN: 978-3-319-97785-0
eBook Packages: Computer ScienceComputer Science (R0)