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

Ruotong Li1,2, Weixin Si3, Michael Weinmann4

  • 1Institute of Computer Science II, University of Bonn, Endenicher Allee 19a, 53115 Bonn, Germany. liruoton@cs.uni-bonn.de.

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Summary
This summary is machine-generated.

This study enhances human skeleton extraction from depth cameras by using length and symmetry rules. The method improves pose accuracy and continuity for moving individuals, overcoming common challenges like occlusions and sensor noise.

Keywords:
consistencycontinuityhuman skeleton extractionskeleton constraints

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Area of Science:

  • Computer Vision
  • Graphics
  • Human Pose Estimation

Background:

  • Human skeleton extraction from single-depth cameras is challenging due to occlusions, appearance variations, and sensor noise.
  • Existing SDKs often produce inaccurate pose estimates lacking physical constraints.

Purpose of the Study:

  • To improve the accuracy and temporal consistency of human skeleton extraction from single-depth cameras.
  • To incorporate physical priors like skeleton length conservation and symmetry into pose estimation.

Main Methods:

  • Incorporating skeleton length conservation and symmetry priors.
  • Utilizing temporal constraints for enhanced consistency.
  • Refining initial pose estimates from SDKs (e.g., Kinect, Nuitrack).

Main Results:

  • Demonstrated improved accuracy and robustness in human skeleton estimation.
  • Successfully detected outliers and predicted new joint locations.
  • Showcased device independence and integration capabilities with existing SDKs.

Conclusions:

  • The proposed framework effectively enhances human skeleton extraction by leveraging physical and temporal constraints.
  • The method offers a robust solution for refining pose estimates in challenging conditions.
  • It provides a valuable tool for improving human motion analysis from depth sensor data.