A Real-Time Video Stream Stabilization System Using Inertial Sensor

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.

[1]  Marius Tico,et al.  Constraint motion filtering for video stabilization , 2005, IEEE International Conference on Image Processing 2005.

[2]  Marius Tico,et al.  Constraint translational and rotational motion filtering for video stabilization , 2005, 2005 13th European Signal Processing Conference.

[3]  Filippo Vella,et al.  Digital image stabilization by adaptive block motion vectors filtering , 2002, IEEE Trans. Consumer Electron..

[4]  Seok-Woo Jang,et al.  Adaptive robust estimation of affine parameters from block motion vectors , 2005, Image Vis. Comput..

[5]  S. Erturk Image sequence stabilisation based on Kalman filtering of frame positions , 2001 .

[6]  C. Miro,et al.  Digital video stabilization architecture for low cost devices , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[7]  Irfan A. Essa,et al.  Calibration-free rolling shutter removal , 2012, 2012 IEEE International Conference on Computational Photography (ICCP).

[8]  Michael Gleicher,et al.  Content-preserving warps for 3D video stabilization , 2009, ACM Trans. Graph..

[9]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Dan Schonfeld,et al.  Online Video Stabilization Based on Particle Filters , 2006, 2006 International Conference on Image Processing.

[11]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[12]  Harry Shum,et al.  Full-frame video stabilization with motion inpainting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Saurabh Upadhyay,et al.  Optical Flow Measurement using Lucas Kanade Method , 2013 .

[14]  Massimo Bergamasco,et al.  A Flexible Framework for WideSpectrum VR Development , 2010, PRESENCE: Teleoperators and Virtual Environments.

[15]  A. Bruna,et al.  Video Stabilization through Dynamic Analysis of Frames Signatures , 2007, 2007 Digest of Technical Papers International Conference on Consumer Electronics.

[16]  Joonki Paik,et al.  An adaptive motion decision system for digital image stabilizer based on edge pattern matching , 1992 .

[17]  Gilbert L. Peterson,et al.  Electronic image stabilization using optical flow with inertial fusion , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Andrea Fusiello,et al.  Image stabilization by features tracking , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[19]  Jiajun Bu,et al.  Video stabilization with a depth camera , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.