Optimized Human Skeleton Detection in Complex Background Videos Using MediaPipe: A Progressive Image Enhancement Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F25%3A43933484" target="_blank" >RIV/60461373:22340/25:43933484 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-96380-3_30" target="_blank" >http://dx.doi.org/10.1007/978-3-031-96380-3_30</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-96380-3_30" target="_blank" >10.1007/978-3-031-96380-3_30</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimized Human Skeleton Detection in Complex Background Videos Using MediaPipe: A Progressive Image Enhancement Approach

Popis výsledku v původním jazyce

In the field of gait analysis using human skeleton detection to evaluate the rehabilitation process of patients with musculoskeletal disorders after brain surgeries, MediaPipe, as a tool, has gained popularity among developers for its efficiency, real-time performance, open source nature, and cross-platform compatibility. However, its accuracy diminishes when dealing with complex, real-world data, such as videos with intricate backgrounds. This paper analyzes the internal detection mechanisms of MediaPipe in human pose estimation and proposes an image-processing-based method to maximize its strengths by generating human-body-contour-based region of interest (ROI) from existing information to guide skeleton detection in subsequent frames. This approach mitigates the impact of complex dynamic backgrounds, showing initial improvements in detection accuracy. Additionally, the paper examines other environment-sensitive issues affecting MediaPipe’s performance, such as target brightness and scale, and provides insights into potential enhancements in these areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Název v anglickém jazyce

Optimized Human Skeleton Detection in Complex Background Videos Using MediaPipe: A Progressive Image Enhancement Approach

Popis výsledku anglicky

In the field of gait analysis using human skeleton detection to evaluate the rehabilitation process of patients with musculoskeletal disorders after brain surgeries, MediaPipe, as a tool, has gained popularity among developers for its efficiency, real-time performance, open source nature, and cross-platform compatibility. However, its accuracy diminishes when dealing with complex, real-world data, such as videos with intricate backgrounds. This paper analyzes the internal detection mechanisms of MediaPipe in human pose estimation and proposes an image-processing-based method to maximize its strengths by generating human-body-contour-based region of interest (ROI) from existing information to guide skeleton detection in subsequent frames. This approach mitigates the impact of complex dynamic backgrounds, showing initial improvements in detection accuracy. Additionally, the paper examines other environment-sensitive issues affecting MediaPipe’s performance, such as target brightness and scale, and provides insights into potential enhancements in these areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Research Perspectives on Software Engineering and Systems Design

ISBN

978-3-031-96379-7

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

8

Strana od-do

343-350

Název nakladatele

Springer Cham

Místo vydání

Cham

Místo konání akce

Online

Datum konání akce

23. 10. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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