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Video stabilization based on saliency driven SIFT matching and discriminative RANSAC

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Xianming LiuAuthors Info & Claims

ICIMCS '11: Proceedings of the Third International Conference on Internet Multimedia Computing and Service

Pages 65 - 69

https://doi.org/10.1145/2043674.2043693

Published: 05 August 2011 Publication History

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Abstract

Inspired by the stability functions of human vision system, we present a novel video stabilization method based on saliency driven SIFT matching and discriminative RANSAC. Firstly, a saliency detection method is adopted to estimate the spatial distribution of visual attention degrees in each frame of the video, and the SIFT features are extracted from the salient regions indicated by the saliency map. Then, we further achieve a modified version of RANSAC method using the discriminative features to estimate the trajectory of inter-frame motion and reduce the errors caused by the foreground vector. Finally, Kalman filter is applied to complete the motion smoothing task. Experimental results demonstrate that our approach is efficient and promising compared with state-of-the-art methods.

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Cited By

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Liu TWan GBai HKong XTang BWang F(2024)Real-Time Video Stabilization Algorithm Based on SuperPointIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.334284973(1-13)Online publication date: 2024
https://doi.org/10.1109/TIM.2023.3342849
Gao FMei KWeng LZhuang Y(2023)Whether and how is a surveillance camera jittering? A ROR perception based framework and methodApplied Intelligence10.1007/s10489-023-04631-353:19(22105-22116)Online publication date: 21-Jun-2023
https://doi.org/10.1007/s10489-023-04631-3
Eustice DKoziol S(2014)Efficient SIFT processing using sub-sampled convolution and masking techniques2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2014.6974018(852-857)Online publication date: Oct-2014
https://doi.org/10.1109/SMC.2014.6974018

Index Terms

Video stabilization based on saliency driven SIFT matching and discriminative RANSAC
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

ICIMCS '11: Proceedings of the Third International Conference on Internet Multimedia Computing and Service

August 2011

208 pages

ISBN:9781450309189

DOI:10.1145/2043674

General Chairs:
Changsheng Xu
Institute of Automation, CAS, China
,
Ralf Steinmetz
Darmstadt Technical University, Germany
,
Program Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada
,
Chong-Wah Ngo
City University of Hong Kong, Hong Kong
,
Xiao Wu
Southwest Jiaotong University, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 05 August 2011

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NEC cooperative project
National Natural Science Foundation of China

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ICIMCS '11

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ICIMCS '11: The Third International Conference on Internet Multimedia Computing and Service

August 5 - 7, 2011

Chengdu, China

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Liu TWan GBai HKong XTang BWang F(2024)Real-Time Video Stabilization Algorithm Based on SuperPointIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.334284973(1-13)Online publication date: 2024
https://doi.org/10.1109/TIM.2023.3342849
Gao FMei KWeng LZhuang Y(2023)Whether and how is a surveillance camera jittering? A ROR perception based framework and methodApplied Intelligence10.1007/s10489-023-04631-353:19(22105-22116)Online publication date: 21-Jun-2023
https://doi.org/10.1007/s10489-023-04631-3
Eustice DKoziol S(2014)Efficient SIFT processing using sub-sampled convolution and masking techniques2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2014.6974018(852-857)Online publication date: Oct-2014
https://doi.org/10.1109/SMC.2014.6974018

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A fast video stabilization system based on speeded-up robust features

Compressed domain video saliency detection using global and local spatiotemporal features

Saliency Detection: A Self-Adaption Sparse Representation Approach

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