Abstract
This paper analyzes a self-height estimation method from a single-shot image using a convolutional architecture. To estimate the height where the image was captured, the method utilizes object-related scene structure contained in a single image in contrast to SLAM methods, which use geometric calculation on sequential images. Therefore, a variety of application domains from wearable computing (e.g., estimation of wearer’s height) to the analysis of archived images can be considered. This paper shows that (1) fine tuning from a pretrained object-recognition architecture contributes also to self-height estimation and that (2) not only visual features but their location on an image is fundamental to the self-height estimation task. We verify these two points through the comparison of different learning conditions, such as preprocessing and initialization, and also visualization and sensitivity analysis using a dataset obtained in indoor environments.
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
Similar content being viewed by others
Notes
- 1.
Currently, FLIR Systems.
- 2.
https://github.com/utkuozbulak/pytorch-cnn-visualizations was modified and used for the implementation.
References
Caballero, F., Merino, L., Ferruz, J., Ollero, A.: Vision-based odometry and SLAM for medium and high altitude flying UAVs. J. Intell. Robot. Syst. 54(1–3), 137–161 (2009)
Finocchiaro, J., Khan, A.U., Borji, A.: Egocentric height estimation. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1142–1150 (2017). https://doi.org/10.1109/WACV.2017.132
Grabe, V., Bülthoff, H.H., Giordano, P.R.: On-board velocity estimation and closed-loop control of a quadrotor UAV based on optical flow. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 491–497 (2012). https://doi.org/10.1109/ICRA.2012.6225328
Jiang, H., Grauman, K.: Seeing invisible poses: estimating 3D body pose from egocentric video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3501–3509 (2017). https://doi.org/10.1109/CVPR.2017.373
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of the International Symposium on Mixed and Augmented Reality (2007)
Mur-Artal, R., Montiel, J.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015). https://doi.org/10.1109/TRO.2015.2463671
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 618–626 (2017). https://doi.org/10.1109/ICCV.2017.74
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS), pp. 568–576 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceeding of the International Conference on Learning Representations (ICLR) (2015)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. In: Proceedings of the International Conference on Learning Representations (ICLR) (2014)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2016)
Acknowledgements
This work is supported by the Cooperative Intelligence Joint Research Chair with Honda Research Institute Japan Co., Ltd.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Shimonishi, K., Fisher, T., Kawashima, H., Funakoshi, K. (2020). Image2Height: Self-height Estimation from a Single-Shot Image. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_61
Download citation
DOI: https://doi.org/10.1007/978-3-030-41404-7_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41403-0
Online ISBN: 978-3-030-41404-7
eBook Packages: Computer ScienceComputer Science (R0)