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 Articles 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/. Title A name given to the resource High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble Subject The topic of the resource artificial intelligence; deep learning; ensemble; High movement; human segmentation; video instance segmentation Description An account of the resource A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame. It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold. Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames. However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates. The proposed AFE approach addresses the limitations of the NFE method. Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe. We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view. The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each. This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models. The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video. 2022 Tech Science Press. All rights reserved. Creator An entity primarily responsible for making the resource Kim Y.-W.; Byun Y.-C.; Han D.S.; Dominic D.; Cyriac S. Source A related resource from which the described resource is derived Computers, Materials and Continua, Vol-73, No. 3, pp. 4743-4762. Publisher An entity responsible for making the resource available Tech Science Press Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.32604/cmc.2022.028632" target="_blank" rel="noreferrer noopener">https://doi.org/10.32604/cmc.2022.028632</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135060531&amp;doi=10.32604%2Fcmc.2022.028632&amp;partnerID=40&amp;md5=5f84861c79f1e8a86b03c0abbc4200c0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135060531&amp;doi=10.32604%2fcmc.2022.028632&amp;partnerID=40&amp;md5=5f84861c79f1e8a86b03c0abbc4200c0</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 15462218 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article 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., Centre for Digital Innovation, CHRIST (Deemed to be University), Bangalore, 560029, India; Byun Y.-C., Department of Computer Engineering, Jeju National University, Jeju, 63243, South Korea; Han D.S., School of Electronics Engineering, Kyungpook National University, Daegu, 41566, South Korea; Dominic D., Centre for Digital Innovation, CHRIST (Deemed to be University), Bangalore, 560029, India; Cyriac S., Centre for Digital Innovation, CHRIST (Deemed to be University), Bangalore, 560029, India