RT info:eu-repo/semantics/article T1 Lightweight real-time hand segmentation leveraging MediaPipe landmark detection A1 Sánchez Brizuela, Guillermo A1 Cisnal de la Rica, Ana A1 Fuente López, Eusebio de la A1 Fraile Marinero, Juan Carlos A1 Pérez Turiel, Javier K1 Augmented reality K1 Hand segmentation K1 MediaPipe K1 Online processing K1 Semantic segmentation K1 33 Ciencias Tecnológicas AB Real-time hand segmentation is a key process in applications that require human–computer interaction, such as gesture rec-ognition or augmented reality systems. However, the infinite shapes and orientations that hands can adopt, their variabilityin skin pigmentation and the self-occlusions that continuously appear in images make hand segmentation a truly complexproblem, especially with uncontrolled lighting conditions and backgrounds. The development of robust, real-time handsegmentation algorithms is essential to achieve immersive augmented reality and mixed reality experiences by correctlyinterpreting collisions and occlusions. In this paper, we present a simple but powerful algorithm based on the MediaPipeHands solution, a highly optimized neural network. The algorithm processes the landmarks provided by MediaPipe usingmorphological and logical operators to obtain the masks that allow dynamic updating of the skin color model. Differentexperiments were carried out comparing the influence of the color space on skin segmentation, with the CIELab color spacechosen as the best option. An average intersection over union of 0.869 was achieved on the demanding Ego2Hands datasetrunning at 90 frames per second on a conventional computer without any hardware acceleration. Finally, the proposed seg-mentation procedure was implemented in an augmented reality application to add hand occlusion for improved user immer-sion. An open-source implementation of the algorithm is publicly available at https://github.com/itap-robotica-medica/lightweight-hand-segmentation. PB Springer SN 1359-4338 YR 2023 FD 2023 LK https://uvadoc.uva.es/handle/10324/61780 UL https://uvadoc.uva.es/handle/10324/61780 LA eng NO Virtual Reality, 2023. NO Producción Científica DS UVaDOC RD 03-oct-2024