Citation

Sultana, Saima and Mansoor Alam, Muhammad and Che Mustapha, Jawahir and Prasad, Mukesh and Nazim, Sadia and Mohd Su'ud, Mazliham (2025) STARS—Deep Diversified Analysis of Occlusion-Resiliency for Robot Vision in Medical Contexts. IEEE Access, 13. pp. 49750-49766. ISSN 2169-3536

Text STARS—Deep Diversified Analysis of Occlusion-Resiliency for Robot Vision in Medical Contexts.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Occlusion is a critical challenge in Robot vision. Unfavorable circumstances including occlusions must be considered when integrating action recognition techniques into an autonomous robotic system. It becomes more critical when the recognition involves Medical scenarios. Occlusion of certain body parts can disrupt the geometric and temporal stability of recognition, significantly affecting accuracy. Despite its practical significance, occlusion in healthcare scenarios is rarely addressed by existing skeleton-based action recognition techniques. This study proposes STARS (Spatial Temporal Adaptive Recurrent Solution), a neural network-based technique designed for robust occlusion handling in medical robotics. The proposed method effectively handles temporal and spatial occlusions in medical scenarios, outperforming state-of-theart techniques. The efficacy of the proposed model is evaluated using the NTU RGB+D dataset, processed through five schematic configurations: Fundamental, Temporal, Spatial, Calibration, and Appraisal. These configurations target occlusion in distinct ways. The findings reveal that occluded data samples impact recognition accuracy, and the proposed technique achieves results close to non-occluded scenarios. However, occlusion in medical activity detection requires further attention.

Item Type:	Article
Uncontrolled Keywords:	Neural networks, occlusion, robot vision
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Apr 2025 06:36
Last Modified:	30 Apr 2025 06:36
URII:	http://shdl.mmu.edu.my/id/eprint/13744

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