Assessing the Performance of an Automated Video Ground Truthing Application

S. Ralph, J. Irvine, M. Stevens, M. Snorrason, and D. Gwilt

Proceedings of Applied Imagery Pattern Recognition, Washington DC (October, 2004)

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, which requires vast amounts of time. 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 human operators. Additionally, the labor-saving results are also demonstrated.

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