Constrained kernel regression for pose estimation

A constrained kernel regression model is proposed to solve the problem of one-dimensional (1D) pose estimation. Unlike the traditional kernel regression model, a circular constraint is applied to the output of the regression function, i.e. using 2D coordinates on a unit circle as output instead of 1D pose angles from 0 to 360°. The experimental results show that with this constraint, the performance of kernel regression on the 1D pose estimation can be improved significantly, and the constrained kernel regression model can run in real-time.