Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.

3D Pose Nowcasting: Forecast the future to improve the present / Simoni, Alessandro; Marchetti, Francesco; Borghi, Guido; Becattini, Federico; Seidenari, Lorenzo; Vezzani, Roberto; Del Bimbo, Alberto. - In: COMPUTER VISION AND IMAGE UNDERSTANDING. - ISSN 1077-3142. - ELETTRONICO. - 251:(2024), pp. 1-9. [10.1016/j.cviu.2024.104233]

3D Pose Nowcasting: Forecast the future to improve the present

Marchetti, Francesco;Becattini, Federico;Seidenari, Lorenzo
;
Vezzani, Roberto;Del Bimbo, Alberto
2024

Abstract

Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.
2024
251
1
9
Simoni, Alessandro; Marchetti, Francesco; Borghi, Guido; Becattini, Federico; Seidenari, Lorenzo; Vezzani, Roberto; Del Bimbo, Alberto
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S107731422400314X-main.pdf

accesso aperto

Tipologia: Pdf editoriale (Version of record)
Licenza: Open Access
Dimensione 1.22 MB
Formato Adobe PDF
1.22 MB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1402781
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact