Abstract
A real-time digital video stabilization system is proposed to remove unwanted camera shakes and jitters. Firstly, SIFT algorithm is improved to extract and match features between the reference frame and current frame reliably, and then global motion parameters are obtained based on the geometric constraint consistency between feature matches through random sample consensus algorithm. Secondly, multiple evaluation criteria are fused by an adaptive low-pass filter to smooth global motion for obtaining correction vector, which is used to compensate the current frame. Finally, stabilized video is obtained after each frame is completed by combining the texture synthesis method and the spatio-temporal information of video. The objective experiments demonstrate the system can increase the average peak signal-to-noise ratio of jittered videos around 6.12 dB, The subjective experiments demonstrate the system can increase the identification ability and perceptive comfort on video content.
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Yu, J., Luo, Cw., Jiang, C., Li, R., Li, Ly., Wang, Zf. (2015). A Digital Video Stabilization System Based on Reliable SIFT Feature Matching and Adaptive Low-Pass Filtering. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 547. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48570-5_18
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