Video stabilisation with total warping variation model

This study proposes a robust approach to stabilise videos with a new variational minimising model. In video stabilisation, accumulation error often occurs in cascaded transformation chain-based methods. To alleviate accumulation error, a new total warping variation (TWV) model is proposed, which describes the smoothness of stabilised camera motion and calculates all the warping transformations efficiently. After estimating original motion parameters based on a 2D similarity transformation model, the corresponding warping parameters are calculated under the TWV minimising framework, where the separable property of the motion parameters is utilised to obtain a closed-form solution. The proposed method provides robust, smooth and precise motion trajectories after stabilisation. Furthermore, an iterative TWV method is introduced to reduce high-frequency jitters as well as low-frequency motions. Moreover, an online TWV method is presented for a long video sequence streaming by adopting a sliding windowed approach. Experimental results on various shaky video sequences show the effectiveness of the proposed method.