A Video Stabilization Method Based on Improved SIFT
This paper proposes a video stabilization method based on improved SIFT to improve computational efficiency and maintain a good video stabilization effect.Firstly,SIFT is improved and named BO-SIFT(Binarized Octagonal SIFT).The algo-rithm introduces concentric octagonal ring feature descriptors,processes the feature vectors by dimensionality reduction and bina-rization,and then uses Hamming distance for feature point matching,which effectively reduces the description and matching time.Secondly,the BO-SIFT algorithm is applied to video stabilization,extracting the feature points of the video frames for matching and calculating the motion offsets between frames to achieve motion estimation.Afterwards,the estimated motion off-sets are smoothed using a Kalman filter and the video frames are inversely compensated using affine transformation to obtain a sta-bilized image sequence.The experimental results show that the BO-SIFT algorithm reduces the stabilization time by 56.404%compared to the original SIFT algorithm,and the stabilized video of the BO-SIFT algorithm has a higher average peak signal-to-noise ratio compared to the existing better algorithms.In addition,the algorithm in this paper is tested on different videos for video stabilization effects,which also has certain reliability and superiority.
video stabilizationBO-SIFT algorithmdimensionality reductionbinarizationmotion estimationpeak signal-to-noise ratio
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