Automatic Self-Improvement Scheme in Optical Flow-Based Motion Estimation for Sequential Fisheye Images

This paper aims to present an innovative design of motion estimation for sequential fisheye images. This design is an extended version of the original Lucas and Kanade's (LK) concept that used to design for calculating optical flow from general perspective images. The extended design consists of the LK concept and an additional self-improvement mechanism that automatically finds the maximum performance of the estimated motion. This extended scheme works much better than the original LK's idea or some block-based motion estimations. Moreover, to some extent, this proposed method is working extremely well to overcome some critical characteristics of the sequential fisheye images. These characteristics include distortion error on the fisheye image area, inconsistent brightness level, fluctuating number of object motion, changing the shape of object motion, or poor camera stability.