Image mosaicking is the process of mapping an image series onto a common image grid, where the resulting mosaic forms a comprehensive view of the scene. This paper presents a near-real-time, automatic image mosaicking system that is designed to operate in real-world conditions. These conditions include arbitrary camera motion, disturbances from moving objects and annotations, and luminance variations. In the proposed algorithm, matching filters are used in conjunction with automatic corner detection to find several critical points within each image, which are then used to represent the image efficiently and accurately. Numerical techniques are used to distinguish between those points belonging to the actual scene and those resulting from a disturbance, and to determine the movement of the camera. The affine model is used to describe the frame-to-frame differences that result from camera motion. A local-adaptive fine-tuning step is used to correct the approximation error due to the use of the affine model, and to compensate for any luminance variation. The mosaic is constructed progressively as new images are being added. The proposed algorithm has been extensively tested on real-world, monocular video sequences, and it is shown to be very accurate and robust.