Present methods of quantifying the performance of ATR algorithms involves the use of large video datasets that must be truthed by hand, frame-by-frame, requiring vast amounts of time. To reduce this cost, we have developed an application that significantly reduces the cost by only requiring the operator to grade a relatively sparse number of data "keyframes". A correlation-based template matching algorithm computes the best position, orientation and scale when interpolating between keyframes.We demonstrate the performance of the automated truthing application, and compare the results to those of a series of human operator test subjects. The START-generated truth is shown to be very close to the mean truth data given by the human operators. Additionally the savings in labor producing the results is also demonstrated.