We are interested in the recovery of anthropometric dimensions of the human body from calibrated monocular sequences, and their use in multi-target tracking across multiple cameras and identification of individual people. In this paper, we focus on two specific anthropomeasures that are relatively easy to estimate from low-resolution images: stature and shoulder breadth. Precise average estimates are obtained for each anthropomeasure by combining measurements from multiple frames in the sequence. Our contribution is two-fold: (i) a novel technique for automatic and passive estimation of shoulder breadth, that is based on modelling the shoulders as an ellipse, and (ii) a novel method for increasing the accuracy of the mean estimates of both anthropomeasures. The latter is based on the observation that major sources of error in the measurements are landmark localization the 2D image and 3D modelling error, and that both of these are correlated with gait phase and body orientation with respect to the camera. Consequently, estimation error can be significantly reduced via appropriate selection or control of these two variables.