Abstract
This paper has the purpose to show a stabilization video-streaming methodology feasible to low-power wearable devices. Thanks to an Inertial Measurement Unit (IMU) mounted together with the camera we are ready to stabilize directly on video-stream without the delay and the complexity due to image processing used by classic software stabilization techniques. The IMU gives information about the angle rotation respect to the three main orthogonal axes of the camera; the wearable device transmits the video along with the IMU data synchronized frame per frame then a base station receives and stabilizes while renders the video. The result is that the shaking and the unwanted motions of the human body wearing the system are compensated giving a clear and stable video. Numeric results prove that the video is more stable: we cut-off the half of the motion noise in the scene.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Auberger, S., Miro, C.: Digital video stabilization architecture for low cost devices. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, p. 474 (2005)
Jang, S.-W., Pomplun, M., Kim, G.-Y., Choi, H.-I.: Adaptive robust estimation of affine parameters from block motion vectors. Image Vis. Comput. 23, 1250–1263 (2005)
Vella, F., Castorina, A., Mancuso, M., Messina, G.: Digital image stabilization by adaptive block motion vectors filtering. IEEE Trans. Consum. Electron. 48, 796–801 (2002)
Bosco, A., Bruna, A., Battiato, S., Bella, G.D.: Video stabilization through dynamic analysis of frames signatures. In: IEEE International Conference on Consumer Electronics (2006)
Censi, A., Fusiello, A., Roberto, V.: Image stabilization by features tracking. In: International Conference on Image Analysis and Processing (1999)
Erturk, S.: Image sequence stabilization based on Kalman filtering of frame positions. Electron. Lett. 37, 1217–1219 (2001)
Paik, J., Park, Y.C., Kim, D.W.: An adaptive motion decision system for digital image stabilizer based on edge pattern matching. In: Consumer Electronics, Digest of Technical Papers, pp. 318–319 (1992)
Tico, M., Vehvilainen, M.: Constraint translational and rotational motion filtering for video stabilization. In: Proceedings of the 13th European Signal Processing Conference (EUSIPCO) (2005)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Yang, J., Schonfeld, D., Chen, C., Mohamed, M.: Online video stabilization based on particle filters. In: IEEE International Conference on Image Processing (2006)
Matsushita, Y., Ofek, E., Ge, W., Tang, X., Shum, H.Y.: Full-frame video stabilization with motion inpainting. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1150–1163 (2006)
Grundmann, M., Kwatra, V., Castro, D., Essa, I.: Calibration-free rolling shutter removal. In: Proceedings of the ICCP (2012)
Liu, F., Gleicher, M., Jin, H., Agarwala, A.: Content preserving warps for 3D video stabilization. In: ACM Transactions on Graphics (Proceedings of SIGGRAPH), vol. 28 (2009)
Liu, S., Wang, Y., Yuan, L., Bu, J., Tan, P., Sun, J.: Video stabilization with a depth camera. In: Proceedings of the CVPR (2012)
Patel, D., Upadhyay, S.: Optical flow measurement using Lucas Kanade method. Int. J. Comput. Appl. 61, 6–10 (2013)
Shi, T.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition (1994)
Tecchia, F., Carrozzino, M., Bacinelli, S., et al.: A flexible framework for wide-spectrum VR development. Presence Teleoperators Virtual Environ. 19(4), 302–312 (2010)
Karpenko, A., Jacobs, D., et al.: Digital video stabilization and rolling shutter correction using gyroscopes. Stanford Tech Report CSTR (2011)
Smith, M.J., Boxerbaum, A., Peterson, G.L., Quinn, R.D.: Electronic image stabilization using optical flow with inertial fusion. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, pp. 1146–1153 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Longobardi, A., Tecchia, F., Carrozzino, M., Bergamasco, M. (2019). A Real-Time Video Stream Stabilization System Using Inertial Sensor. In: De Paolis, L., Bourdot, P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2019. Lecture Notes in Computer Science(), vol 11613. Springer, Cham. https://doi.org/10.1007/978-3-030-25965-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-25965-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-25964-8
Online ISBN: 978-3-030-25965-5
eBook Packages: Computer ScienceComputer Science (R0)