Kinematic target surface sensing based on improved deep optical flow tracking

Abstract

Reconstruction of moving target surfaces based on active image sensing techniques, such as phase-shifting profilometry, has attracted intensive research in recent years. The measurement error caused by object motion can be addressed successfully by tracking the object movement. However, it either requires high-cost color imaging equipment or is limited by the assumption of 2D translation movement. Therefore, this paper proposes what we believe to be a new method to reconstruct the kinematic object surfaces with any 2D movement sensed by affordable monochrome camera. An improved RAFT optical flow algorithm is proposed to track the object based on the object fringe pattern image directly. The feature points on the object are retrieved immune to the fringe pattern illumination. Then, the RANSAC algorithm and an iteration selection process are employed to select feature points with high quality optical flow. At last, the motion is described mathematically, and the dynamic object is reconstructed successfully. Experiments are presented to verify the effectiveness of the proposed method.