Search

We propose a method that dramatically improves the performance of template-based matching in terms of size of convergence region and computation time. This is done by selecting a subset of the template that verifies the assumption (made during optimization) of linearity or quadraticity with respect to the motion parameters. We call these subsets linear or quadratic subsets. While subset selection approaches have already been proposed, they generally do not attempt to provide linear or quadratic subsets and rely on heuristics such as textured-ness. Because a naive search for the optimal subset would result in a combinatorial explosion for large templates, we propose a simple algorithm that does not aim for the optimal subset but provides a very good linear or quadratic subset at low cost, even for large templates. Simulation results and experiments with real sequences show the superiority of the proposed method compared to existing subset selection approaches.

We study from a theoretical standpoint the ambiguities that occur when tracking a generic deformable surface under monocular perspective projection given 3D to 2D correspondences. We show that, additionally to the known scale ambiguity, a set of potential ambiguities can be clearly identified. From this, we deduce a minimal set of constraints required to disambiguate the problem and incorporate them into a working algorithm that runs on real noisy data.

We combine detection and tracking techniques to achieve robust 3-D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to infer 3-D poses between consecutive detections, and finally refining them over the whole sequence using a generative model. We demonstrate our approach in the case of people walking against cluttered backgrounds and filmed using a moving camera, which precludes the use of simple background subtraction techniques. In this case, the easy-to-detect posture is the one that occurs at the end of each step when people have their legs furthest apart.

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well to handle large number of classes. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.