We consider the problem of using either grouped observations of a counting process over a period or prescribed constant mean counts to create an intensity function for a Non-Homogeneous Poisson Process (NHPP) that estimates the observed process, and satisfies several constraints. First, we require that the estimator's mean value over an observation interval be equal to the mean number of observations in that interval; second, we require that the intensity function be continuous; third, we require that the function be piecewise linear. Optionally, we may also require that the intensity value at the end of the period be identical to the intensity value at the beginning of the period, for application in contexts in which the period of interest is inherently cyclic, e.g., a day, or a week. An objective of the estimator is that it should be 'smooth,' which will be defined subsequently. Our approach is to define a class of continuous piecewise-linear intensity functions and formulate the problem as a constrained quadratic programming problem, approachable through the solution of a simultaneous set of linear equations. We describe the method, identify conditions under which feasible solutions are assured to exist, and study the behavior of the solutions on an example problem.