This paper describes the theory and a practical algorithm for the autocalibration of a moving projective camera, from m ≥ 5 views of a planar scene. The unknown camera calibration, motion and scene geometry are recovered up to scale, from constraints encoding the motion-invariance of the camera's internal parameters. This extends the domain of autocalibration from the classical non-planar case to the practically common planar one, in which the solution can not be bootstrapped from an intermediate projective reconstruction. It also generalizes Hartley's method for the internal calibration of a rotating camera, to allow camera translation and to provide 3D as well as calibration information. The basic constraint is that orthogonal directions (points at infinity) in the plane must project to orthogonal directions in the calibrated images. Abstractly, the plane's two circular points (representing its Euclidean structure) lie on the 3D absolute conic, so their projections must lie on the absolute image conic (representing the camera calibration). The resulting algorithm optimizes this constraint numerically over all circular points and all projective calibration parameters, using the inter-image homographies as a projective scene representation.