Online learning of patch perspective rectification for efficient object detection

For a large class of applications, there is time to train the system. In this paper, we propose a learning-based approach to patch perspective rectification, and show that it is both faster and more reliable than state-of-the-art ad hoc affine region detection methods. Our method performs in three steps. First, a classifier provides for every keypoint not only its identity, but also a first estimate of its transformation. This estimate allows carrying out, in the second step, an accurate perspective rectification using linear predictors. We show that both the classifier and the linear predictors can be trained online, which makes the approach convenient. The last step is a fast verification -made possible by the accurate perspective rectification- of the patch identity and its sub-pixel precision position estimation. We test our approach on real-time 3D object detection and tracking applications. We show that we can use the estimated perspective rectifications to determine the object pose and as a result, we need much fewer correspondences to obtain a precise pose estimation.