Improved feature detection algorithm for hand in augmented assembly

Aiming at the hand feature detection and recognition problem for the augmented assembly guide process, an improved algorithm based on Continuously adaptive mean-Shift (CamShift) was proposed. In this algorithm, the hand was traced and detected in the assembly environment, the search box was obtained by iterative computing the center distance, and complete information of hand dynamic optimization detection area to get. To get the features of hand, by using hand outline with image enhancement processed, the feature points with equidistance was sampled, the curvatures for different position of feature points were calculated, and a group fingertip points was selected after clustering analysis. A prototype system was developed by using 3D registration of augmented assembly as an example to verify the effectiveness, feasibility and better robustness of proposed method.