Highly Articulated Kinematic Structure Estimation Combining Motion and Skeleton Information

Abstract

Abstract-In this paper, we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view 2D image sequence. In contrast to prior motion-based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology via a successive iterative merging strategy. The iterative merge process is guided by a density weighted skeleton map which is generated from a novel object boundary generation method from sparse 2D feature points. Our main contributions can be summarised as follows: (i) An unsupervised complex articulated kinematic structure estimation method that combines motion segments with skeleton information. (ii) An iterative fine-to-coarse merging strategy for adaptive motion segmentation and structural topology embedding. (iii) A skeleton estimation method based on a novel silhouette boundary generation from sparse feature points using an adaptive model selection method. (iv) A new highly articulated object dataset with ground truth annotation. We have verified the effectiveness of our proposed method in terms of computational time and estimation accuracy through rigorous experiments with multiple datasets. Our experiments show that the proposed method outperforms state-of-the-art methods both quantitatively and qualitatively.Index Terms-Highly articulated kinematic structure estimation, adaptive motion segmentation, density weighted silhouette generation from sparse points, adaptive kernel selection.

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

“…The kinematic structure learning functional module [95], [96] estimates an articulated kinematic structure of arbitrary objects (including the robot's body parts and humans) using visual input videos of the iCub eye cameras. This again is based on sensory experiences rather than known properties of the agents, which is important to autonomously identify the abilities of other agents.…”

“…This again is based on sensory experiences rather than known properties of the agents, which is important to autonomously identify the abilities of other agents. Based on the estimated articulated kinematic structures [95], we also allow the iCub to anchor two objects' kinematic structure joints by observing their movements [96] and formulating the problem of finding corresponding kinematic joint matches between two articulated kinematic structures. This allows the iCub to infer correspondences between its own body parts (its left arm and its right arm), as well as between its own body and the body of the human as retrieved by the agent detector [93] (see Figure 2).…”

Skeletal Structure and Training Adaptability of Athletes Based on Biomechanical Analysis

“…The material mechanical properties of bone refer to the mechanical properties of bone tissue itself, which are independent of the geometric shape of bone. The material mechanical properties of bone can be reflected by stress-strain curves [ 17 ]. This paper mainly outlines the mechanism of adaptive change and regulation of mechanical stimulation (stress) or physical exercise on bone-bone mineral density and structure and briefly introduces the modern measurement techniques and methods of related research and evaluation indicators [ 18 ].…”

Ghost shuffle lightweight pose network with effective feature representation and learning for human pose estimation

“…The task of two‐dimensional (2‐D) human pose estimation is to localise human anatomical keypoints (e.g., elbow, wrist) or body parts from an image, which is fundamental to a variety of vision applications, including human action recognition [1–3], kinematics analysis [4], human–computer interaction [5, 6], and animation. Owing to the complex background environment, non‐rigid properties, and occlusions of the human body, human pose estimation is a challenging but important problem.…”