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In anatomy, several standard anatomical positions are used as references for describing the position and orientation of different body parts. These positions help provide a common frame of reference when discussing anatomical structures. The anatomical position is the standard reference point for describing the body's position and orientation. In this position:
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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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Beyond Human Detection: A Benchmark for Detecting Common Human Posture.

Yongxin Li1, You Wu1, Xiaoting Chen1

  • 1Guangxi Key Laboratory of Embedded Technology and Intelligent Information Processing, College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the Common Human Postures (CHP) dataset for advanced human pose estimation. The new dataset and baseline detectors show effectiveness in recognizing human postures beyond simple detection.

Keywords:
CHP datasetbenchmarkcommon human posture detectionhuman detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Human detection is vital for applications like surveillance and autonomous driving.
  • Advanced applications require deeper understanding of human behavior and state beyond localization.
  • Current methods lack the detail needed for complex human-computer interaction and scene understanding.

Purpose of the Study:

  • Introduce the Common Human Postures (CHP) dataset as a benchmark for human pose estimation.
  • Promote research into more informative tasks beyond basic human detection.
  • Facilitate the development of novel methods for precise human posture recognition.

Main Methods:

  • Collected and annotated 5250 human images across diverse scenarios, including various environments, clothing, and occlusions.
  • Annotated images with bounding boxes for seven common human poses.
  • Developed two baseline detectors, CHP-YOLOF and CHP-YOLOX, based on identity-preserved human posture detectors.

Main Results:

  • Evaluated baseline detectors (CHP-YOLOF, CHP-YOLOX) on the CHP dataset through extensive experiments.
  • Demonstrated the effectiveness of the developed detectors in accurately identifying human postures.
  • Established a benchmark for performance evaluation in human pose estimation tasks.

Conclusions:

  • The CHP dataset provides a valuable resource for advancing human pose estimation research.
  • The developed baseline detectors show promising performance on the new benchmark.
  • Encourages further research and focus on the challenging task of detailed human posture analysis.