Structural SVM for visual localization and continuous state estimation

Abstract

We present an integrated model for visual object localization and continuous state estimation in a discriminative structured prediction framework. While existing discriminative `prediction through time' methods have showed remarkable versatility for visual reconstruction and tracking problems, they tend to assume that the input is known (or the object is segmented) a condition that can rarely be accommodated in images of real scenes. Our structural Support Vector Machine (structSVM) framework offers an end-to-end training and inference framework that overcomes these limitations by consistently searching both in the space of possible inputs (effectively an efficient form of object localization) and in the space of possible structured outputs, given those inputs. We demonstrate the potential of this methodology for 3d human pose reconstruction in monocular images both in the HumanEva benchmark, where 3d ground truth is available, and qualitatively, in un-instrumented images of real scenes.