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Open AccessArticle

Constraint-Based Optimized Human Skeleton Extraction from Single-Depth Camera

Institute of Computer Science II, University of Bonn, Endenicher Allee 19a, 53115 Bonn, Germany
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen 518055, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2604;
Received: 9 May 2019 / Revised: 4 June 2019 / Accepted: 5 June 2019 / Published: 7 June 2019
(This article belongs to the Special Issue Sensors for Biomechanics Application)
As a cutting-edge research topic in computer vision and graphics for decades, human skeleton extraction from single-depth camera remains challenging due to possibly occurring occlusions of different body parts, huge appearance variations, and sensor noise. In this paper, we propose to incorporate human skeleton length conservation and symmetry priors as well as temporal constraints to enhance the consistency and continuity for the estimated skeleton of a moving human body. Given an initial estimation of the skeleton joint positions provided per frame by the Kinect SDK or Nuitrack SDK, which do not follow the aforementioned priors and can prone to errors, our framework improves the accuracy of these pose estimates based on the length and symmetry constraints. In addition, our method is device-independent and can be integrated into skeleton extraction SDKs for refinement, allowing the detection of outliers within the initial joint location estimates and predicting new joint location estimates following the temporal observations. The experimental results demonstrate the effectiveness and robustness of our approach in several cases. View Full-Text
Keywords: human skeleton extraction; skeleton constraints; consistency; continuity human skeleton extraction; skeleton constraints; consistency; continuity
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Li, R.; Si, W.; Weinmann, M.; Klein, R. Constraint-Based Optimized Human Skeleton Extraction from Single-Depth Camera. Sensors 2019, 19, 2604.

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