Real-Time Video Stabilization Based on Smoothing Feature Trajectories

2014 ◽  
Vol 519-520 ◽  
pp. 640-643 ◽  
Author(s):  
Jing Dong ◽  
Yang Xia

In this paper, a real-time video stabilization algorithm based on smoothing feature trajectories is proposed. For each input frame, our approach generates multiple feature trajectories by performing inter-frame template match and optical flow. A Kalman filter is then performed to smooth these feature trajectories. Finally, at the stage of image composition, the motion consistency of the feature trajectory is considered for achieving a visually plausible stabilized video. The proposed method can offer real-time video stabilization and its removed the delays for caching coming images. Experiments show that our approach can offer real-time stabilizing for videos with various complicated scenes.

2018 ◽  
Vol 47 (1) ◽  
pp. 110002 ◽  
Author(s):  
谢亚晋 XIE Ya-jin ◽  
徐之海 XU Zhi-hai ◽  
冯华君 FENG Hua-jun ◽  
李奇 LI Qi ◽  
陈跃庭 CHEN Yue-ting

2017 ◽  
Vol 16 (6) ◽  
pp. 1975-1985 ◽  
Author(s):  
Anli Lim ◽  
Bharath Ramesh ◽  
Yue Yang ◽  
Cheng Xiang ◽  
Zhi Gao ◽  
...  

2014 ◽  
Vol 596 ◽  
pp. 316-321
Author(s):  
Yan Ni Wang

Owing to the problems of inter frame difference method cannot extract the entire target and most optical flow algorithms with computational time, poor real-time performance, a local optical flow constraint target extracting algorithm of Kalman filter based on background modeling is proposed. Firstly use Kalman filter method based on background modeling predict and update the background, then make Lucas-Kanade local optical flow algorithm search the background changing region, finally determine the gray contour, extract target. Compared with the classical algorithms, the simulation results show that new algorithm can extract the moving object quickly and accurately, and has better robustness to the environment changes and target circuitous movement.


Author(s):  
A. V. Bratulin ◽  
◽  
M. B. Nikiforov ◽  
A. I. Efimov ◽  
◽  
...  

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