The 'Matchmove', or camera-tracking process is a crucial task and one of the first to be performed in the visual effects pipeline. An accurate solve for camera movement is imperative and will have an impact on almost every other part of the pipeline downstream. In this work we present a comprehensive analysis of the process at a major visual effects studio, drawing on a large dataset of real shots. We also present guidelines and rules-of-thumb for camera tracking scheduling which are, in what we believe to be an industry first, backed by statistical data drawn from our dataset. We also make available data from our pipeline which shows the amount of time spent on camera tracking and the types of shot that are most common in our work. We hope this will be of interest to the wider computer vision research community and will assist in directing future research.

Estimating changes in camera parameters, such as motion, focal length and exposure time over a single frame or sequence of frames is an integral part of many computer vision applications. Rapid changes in these parameters often cause motion blur to be present in an image, which can make traditional methods of feature identification and tracking difficult. Here we present a method for estimating the scale changes brought about by change in focal length from a single motion-blurred frame. We also use the results from two seperate methods for determining the rotation of a pair of motion-blurred frames to estimate the exposure time of a frame (i.e. the shutter angle).

a b s t r a c tEstimating changes in camera parameters, such as motion, focal length and exposure time over a single frame or sequence of frames, is an integral part of many computer vision applications. Rapid changes in these parameters often cause motion blur to be present in an image, which can make traditional methods of feature identification and tracking difficult. In this work we describe a method for tracking changes in two camera intrinsic parameters -shutter angle and scale changes brought about by changes in focal length. We also provide a method for estimating the expected accuracy of the results obtained using these methods and evaluate how the technique performs on images with a low depth of field, and therefore likely to contain blur other than that brought about by motion.