This paper investigates the application of total least squares (TLS) technique in conditioning optical flow field estimates obtained from gradient based optical flow constraints. Optical flow field processing has been applied to perform moving target indication (MTI) for IR/TV sensors but results can be severely degraded in noisy imagery. The usual solution is to apply some form of nonstatistical pre-processing to the input image intensities or statistical post- processing spatial smoothing, such as least squares (LS) fitting, to the output optical flow field vectors to suppress noise. However, LS solution is known for generating biased optical flow field vector estimate in noisy imagery due to spatial gradient matrix noise. Our empirical results show improved performance of TLS over LS at lower SNRs. Results are presented in terms of optical flow field accuracy measures and target detection rates, for synthetic imagery and real infrared imagery.