Robust digital image stabilization using feature tracking

Chuntao Wang; Jin Hyung Kim; Keun Yung Byun; Sung-Jea Ko

doi:10.1109/ICCE.2009.5012257

Robust digital image stabilization using feature tracking

Chuntao Wang, Jin Hyung Kim, Keun Yung Byun, Sung-Jea Ko

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This paper presents a new robust digital image stabilization (DIS) algorithm which uses well-tracked feature points to estimate the motion between two consecutive frames. The motion prediction with the Kalman filter is incorporated into the Kanade-Lucas-Tomasi (KLT) tracker to further speed up the tracking process. A new scheme is proposed to adaptively update the Kalman filter. Simulation results show that the proposed algorithm can speed up the tracking process and obtain more stabilized performance.

Original language	English
Title of host publication	Digest of Technical Papers - IEEE International Conference on Consumer Electronics
DOIs	https://doi.org/10.1109/ICCE.2009.5012257
Publication status	Published - 2009 Sep 25
Event	2009 International Conference on Consumer Electronics, ICCE 2009 - Las Vegas, NV, United States Duration: 2009 Jan 10 → 2009 Jan 14

Other

Other	2009 International Conference on Consumer Electronics, ICCE 2009
Country	United States
City	Las Vegas, NV
Period	09/1/10 → 09/1/14

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ASJC Scopus subject areas

Electrical and Electronic Engineering
Industrial and Manufacturing Engineering

Cite this

Wang, C., Kim, J. H., Byun, K. Y., & Ko, S-J. (2009). Robust digital image stabilization using feature tracking. In Digest of Technical Papers - IEEE International Conference on Consumer Electronics [5012257] https://doi.org/10.1109/ICCE.2009.5012257

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AB - This paper presents a new robust digital image stabilization (DIS) algorithm which uses well-tracked feature points to estimate the motion between two consecutive frames. The motion prediction with the Kalman filter is incorporated into the Kanade-Lucas-Tomasi (KLT) tracker to further speed up the tracking process. A new scheme is proposed to adaptively update the Kalman filter. Simulation results show that the proposed algorithm can speed up the tracking process and obtain more stabilized performance.

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Access to Document

10.1109/ICCE.2009.5012257