Robust 3D object tracking with X-Triplet markers: A machine learning-based approach

Object tracking in 3D space is a classical problem in computer vision. In this paper, an efficient and robust X-Triplet detection method is proposed based on the support vector machine (SVM) and an adjacent matrix for locating and tracking objects through stereo vision with minimal feature points. The X-Triplet, denoted as Tri-X, is a composite marker consisting of three sequential X-corners. The definition and types of Tri-X markers are introduced at first. Then a fast and robust X-corner detector based on the block search strategy and SVM is proposed to extract X-corner candidates with sub-pixel locations and orientations. Thereafter the X-corner adjacent matrix (XAM) is constructed using the orientation angle error to describe the possibility that any X-corner pair form a valid edge vector. The Tri-X candidates are then extracted efficiently from the XAM. Finally once the Tri-X markers are detected in binocular images, their 6D pose information can be recovered through stereo matching and triangulation technique. When multiple targets are involved simultaneously, different Tri-X markers can be utilized to identify different objects. Experimental results show that the proposed method outperformed the state-of-the-art in terms of both accuracy and efficiency for Tri-X marker detection. In localization precision test, it achieved 0.1 mm error for the position and 1° error for the orientation. Our method exhibits great potential for utilization in user-defined specific tracking tasks, offering flexibility and adaptability to various tracking requirements, especially multi-tool tracking in medical robotics.