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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion.

Zhigang Hu¹, Chengwu Zhang¹, Xinzheng Wang¹

¹College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471023, China.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

This study introduces a new algorithm for accurately detecting human key points, even with changing lights and motion. It fuses depth camera and Mediapipe data for robust performance in rehabilitation and HCI.

Keywords:

information fusion key point detection lighting adaptation

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Area of Science:

Computer Vision
Biomedical Engineering
Human-Computer Interaction

Background:

Accurate human key point detection is crucial for applications like sports rehabilitation and medical diagnosis.
Existing methods like depth cameras and the Mediapipe algorithm have limitations, particularly with varying lighting conditions and human motion.
Challenges include maintaining accuracy despite illumination changes and motion-induced point variations.

Purpose of the Study:

To develop an illumination-adaptive algorithm for robust human key point detection.
To fuse multi-source information for enhanced accuracy and stability.
To address the limitations of current methods in dynamic and variable lighting environments.

Main Methods:

Fusion of key point data from depth cameras and the Mediapipe algorithm.
Development of an illumination change model to simulate lighting variations.
Solving the fitting function for lighting conditions and adaptive weights to achieve illumination adaptation.

Main Results:

High R2 values (0.96 and 0.93) and cosine similarity (0.92 and 0.90) compared to benchmark data.
Joint accuracy rates of 89% and 88% for two rehabilitation actions within an 8-degree threshold.
Demonstrated stability under changing illumination and effective anti-shake capabilities for human movement.

Conclusions:

The proposed method offers stable and accurate human key point detection under challenging, variable illumination conditions.
The algorithm exhibits strong anti-shake capabilities, making it suitable for dynamic human motion analysis.
Promising applications in human-computer interaction, sports rehabilitation, and virtual reality environments.