2134/14645 Baihua Li Baihua Li Qinggang Meng Qinggang Meng Horst Holstein Horst Holstein Articulated motion reconstruction from feature points Loughborough University 2014 Non-rigid articulated motion Point pattern matching Non-rigid pose estimation Motion tracking and object recognition Artificial Intelligence and Image Processing Information and Computing Sciences not elsewhere classified 2014-05-21 13:30:22 Journal contribution https://repository.lboro.ac.uk/articles/journal_contribution/Articulated_motion_reconstruction_from_feature_points/9401408 A fundamental task of reconstructing non-rigid articulated motion from sequences of unstructured feature points is to solve the problem of feature correspondence and motion estimation. This problem is challenging in high-dimensional configuration spaces. In this paper, we propose a general model-based dynamic point matching algorithm to reconstruct freeform non-rigid articulated movements from data presented solely by sparse feature points. The algorithm integrates key-frame-based self-initialising hierarchial segmental matching with inter-frame tracking to achieve computation effectiveness and robustness in the presence of data noise. A dynamic scheme of motion verification, dynamic key-frame-shift identification and backward parent-segment correction, incorporating temporal coherency embedded in inter-frames, is employed to enhance the segment-based spatial matching. Such a spatial–temporal approach ultimately reduces the ambiguity of identification inherent in a single frame. Performance evaluation is provided by a series of empirical analyses using synthetic data. Testing on motion capture data for a common articulated motion, namely human motion, gave feature-point identification and matching without the need for manual intervention, in buffered real-time. These results demonstrate the proposed algorithm to be a candidate for feature-based real-time reconstruction tasks involving self-resuming tracking for articulated motion.