Robust monocular object pose tracking for large pose shift using 2D tracking

Abstract

Abstract Monocular object pose tracking has been a key technology in autonomous rendezvous of two moving platforms. However, rapid relative motion between platforms causes large interframe pose shifts, which leads to pose tracking failure. Based on the derivation of the region-based pose tracking method and the theory of rigid body kinematics, we put forward that the stability of the color segmentation model and linearization in pose optimization are the key to region-based monocular object pose tracking. A reliable metric named VoI is designed to measure interframe pose shifts, based on which we argue that motion continuity recovery is a promising way to tackle the translation-dominant large pose shift issue. Then, a 2D tracking method is adopted to bridge the interframe motion continuity gap. For texture-rich objects, the motion continuity can be recovered through localized region-based pose transferring, which is performed by solving a PnP (Perspective-n-Point) problem within the tracked 2D bounding boxes of two adjacent frames. Moreover, for texture-less objects, a direct translation approach is introduced to estimate an intermediate pose of the frame. Finally, a region-based pose refinement is exploited to obtain the final tracked pose. Experimental results on synthetic and real image sequences indicate that the proposed method achieves superior performance to state-of-the-art methods in tracking objects with large pose shifts.