Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kim, Yong-Woon; Byun, Yung-Cheol; Krishna, Addapalli V.N.; Krishnan, Balachandran Title A name given to the resource High-speed portrait video segmentation using the hybrid combination of deep-learning models and boundary movement adjustment Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Engineering Applications of Artificial Intelligence;Volume;155;Issue;;Article No.;111077; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.engappai.2025.111077" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.engappai.2025.111077</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005080347?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005080347?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kim Y.-W., Department of Data Science, CHRIST (Deemed to be University) Lavasa, Pune, India; Byun Y.-C., Department of Computer Engineering, Major of Electronic Engineering, Jeju National University, Jeju, South Korea; Krishna A.V.N., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Krishnan B., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India Description An account of the resource As global warming intensifies, the development of energy-efficient Artificial Intelligence (AI) technologies has become crucial. Additionally, the growing demand for on-device AI in smartphones, extended reality devices, and autonomous vehicles necessitates AI systems that can function effectively on low-performance hardware. To address these needs, this study proposes hybrid methods in the field of Portrait Video Segmentation (PVS). Our proposed hybrid models leverage Deep-learning based Segmentation Models (DSMs) and a novel Boundary Movement Adjustment (BMA) process to achieve speed and accuracy balance. The Hybrid Serial Model (HSM) not only accelerates PVS but also improves energy efficiency while maintaining a similar level of accuracy. On the other hand, the Hybrid Parallel Model (HPM) enables high-performance PVS even on low-performance devices, ensuring no video frames are lost during high-speed segmentation processing. Tests conducted on Jetson Nano, Raspberry Pi, and a desktop PC demonstrate the effectiveness of these models, showing improvements in PVS speed while maintaining accuracy close to that of traditional DSMs. HSM increased PVS speed from 15.2 Frames Per Second (FPS) to 25.1 FPS on a desktop PC with a 0.5 % accuracy loss, and from 6.3 FPS to 16.5 FPS on a Jetson Nano with a 1 % loss. HPM reached 30 FPS on a desktop PC with a 0.05 % loss, and 29.7 FPS on a Jetson Nano with a 1 % loss. On the Raspberry Pi, the HPM method improved speed from 2.9 FPS to 29.8 FPS, demonstrating its adaptability for low-performance devices. 2025 Elsevier Ltd Subject The topic of the resource CO₂ emissions; Deep-learning; Energy-efficiency; Global warming; Hybrid; Portrait video segmentation Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 9521976; CODEN: EAAIE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online