Global motion estimation with iterative optimization-based independent univariate model for action recognition

Highlights：

• We propose an iterative optimization scheme for global motion estimation. The outlier points with local and global motion will be gradually discarded by an adaptive threshold during each iteration and the estimation process is implemented in a coarse-to-fine manner. Moreover, the local motion field is estimated based on mixed and estimated global motion fields through a spatio-temporal threshold based scheme.

• We evaluate our proposed global and local motion estimation scheme on action recognition tasks. The separated local motion is adopted as input instead of original mixed motion field. Extensive experiments on multiple deep neural networks and action recognition datasets demonstrate the effectiveness of our proposed method.

摘要

•We deeply analyze the characteristics of global motions in action recognition scenario and develop a novel independent uni-variate model for global motion representation. It is a simplified version of perspective model. The number of its parameters is same as that of affine model. It makes a good trade-off between robustness and complexity which is more suitable for action recognition applications.•We propose an iterative optimization scheme for global motion estimation. The outlier points with local and global motion will be gradually discarded by an adaptive threshold during each iteration and the estimation process is implemented in a coarse-to-fine manner. Moreover, the local motion field is estimated based on mixed and estimated global motion fields through a spatio-temporal threshold based scheme.•We evaluate our proposed global and local motion estimation scheme on action recognition tasks. The separated local motion is adopted as input instead of original mixed motion field. Extensive experiments on multiple deep neural networks and action recognition datasets demonstrate the effectiveness of our proposed method.