A spatially recursive optical flow estimation framework using adaptive filtering

In this paper, we propose a spatially recursive optical flow estimation (OFE) framework using adaptive filtering. One of most successful OFE algorithms is a gradient-based least-squares (LS) within a local image window because of high performance and low-complexity. However, it has some redundancies for calculating successive LS among adjacent pixels. Therefore, we suggest an efficient framework using recursive least-squares (RLS) and adaptive filtering to improve the computational efficiency. The performance and computational complexity are compared to least-squares OFE and spatially recursive OFE algorithms. Based on these results, we conclude that our proposed algorithm framework under proper window size can reduce computational complexity especially as the number of motion modeling parameters increases by using the property of RLS and adaptive filtering.