Global motion estimation based on kalman predictor
Guili Xu, Maoshi Ding, Yuehua Cheng, Yupeng Tian · 2009
In order to detect moving object by a rotated camera in video surveillance, block-based motion estimations (BME) are performed first and global motion parameters are estimated. A novel search algorithm that based on Kalman filter is proposed. The algorithm is a kind of block-matching motion estimation algorithm. First feature points are extracted from current frame and then feature points are used as the central points in block matching between consecutive frames, then the 3sigma rule is used to remove blocks of error. Kalman filter is used to search matching blocks and results have shown that a total decrease by about 95% in computation time is achieved compared to the classical full-search BME process in global motion estimation.