Robust planar target tracking and pose estimation from a single concavity

In this paper we introduce a novel real-time method to track weakly textured planar objects and to simultaneously estimate their 3D pose. The basic idea is to adapt the classic tracking-by-detection approach, which seeks for the object to be tracked independently in each frame, for tracking non-textured objects. In order to robustly estimate the 3D pose of such objects in each frame, we have to tackle three demanding problems. First, we need to find a stable representation of the object which is discriminable against the background and highly repetitive. Second, we have to robustly relocate this representation in every frame, also during considerable viewpoint changes. Finally, we have to estimate the pose from a single, closed object contour. Of course, all demands shall be accommodated at low computational costs and in real-time. To attack the above mentioned problems, we propose to exploit the properties of Maximally Stable Extremal Regions (MSERs) for detecting the required contours in an efficient manner and to apply random ferns as efficient and robust classifier for tracking. To estimate the 3D pose, we construct a perspectively invariant frame on the closed contour which is intrinsically provided by the extracted MSER. In our experiments we obtain robust tracking results with accurate poses on various challenging image sequences at a single requirement: One MSER used for tracking has to have at least one concavity that sufficiently deviates from its convex hull.