A large number of absolute pose algorithms have been presented in the literature. Common performance criteria are computational complexity, geometric optimality, global optimality, structural degeneracies, and the number of solutions. The ability to handle minimal sets of correspondences, resulting solution multiplicity, and generalized cameras are further desirable properties. This paper presents the first PnP solution that unifies all the above desirable properties within a single algorithm. We compare our result to state-of-the-art minimal, non-minimal, central, and non-central PnP algorithms, and demonstrate universal applicability, competitive noise resilience, and superior computational efficiency. Our algorithm is called Unified PnP (UPnP).

Abstract.A popular approach to detect outliers in a data set is to find the largest consensus set, that is to say maximizing the number of inliers and estimating the underlying model. RANSAC is the most widely used method for this aim but is non-deterministic and does not guarantee to return the optimal solution. In this paper, we consider a rotation model and we present a new approach that performs consensus set maximization in a mathematically guaranteed globally optimal way. We solve the problem by a branch-and-bound framework associated with a rotation space search. Our mathematical formulation can be applied for various computer vision tasks such as panoramic image stitching, 3D registration with a rotating range sensor and line clustering and vanishing point estimation. Experimental results with synthetic and real data sets have successfully confirmed the validity of our approach.

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Knowing the locations of the players and the ball on a ground field is important for soccer game analysis. Given an image sequence, we address three main problems: 1) ground field extraction, 2) player and ball tracking and team identification and 3) absolute player positioning. The region of ground field is extracted on the basis of color information, within which all the other processing is restricted. Players are tracked by template matching and Kalman filtering. Occlusion reasoning is done by color histogram back-projection. To find the location of a player, afield model is constructed and a transformation between the input image and the field model is computed using feature points. Otherwise, an image-based mosaicking technique is applied. Using this image-to-model transformation, the absolute positions and the trajectories of players on the field model are determined. We tested our method on real image sequences and the experimental results are given.

Abstract. Identifying inliers and outliers among data is a fundamental problem for model estimation. This paper considers models composed of rotation and focal length, which typically occurs in the context of panoramic imaging. An efficient approach consists in computing the underlying model such that the number of inliers is maximized. The most popular tool for inlier set maximization must be RANSAC and its numerous variants. While they can provide interesting results, they are not guaranteed to return the globally optimal solution, i.e. the model leading to the highest number of inliers. We propose a novel globally optimal approach based on branch-and-bound. It computes the rotation and the focal length maximizing the number of inlier correspondences and considers the reprojection error in the image space. Our approach has been successfully applied on synthesized data and real images.