{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:14:43Z","timestamp":1760228083681,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground\u2013background masks from depth images for human body segmentation. The framework can be used to speed up the manual depth image annotation process with no semantics known beforehand and can apply segmentation using a performant algorithm while the user only adjusts the parameters, or corrects the automatic segmentation results, or gives it hints by drawing a boundary of the desired object. The approach has been tested using two different datasets with a human in a real-world closed environment. The solution has provided promising results in terms of reducing the manual segmentation time from the perspective of the processing time as well as the human input time.<\/jats:p>","DOI":"10.3390\/s22093531","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T23:27:25Z","timestamp":1652052445000},"page":"3531","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1251-1575","authenticated-orcid":false,"given":"Karolis","family":"Ryselis","sequence":"first","affiliation":[{"name":"Faculty of Informatics, Kaunas University of Technology, 44249 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2858-328X","authenticated-orcid":false,"given":"Tomas","family":"Bla\u017eauskas","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, Kaunas University of Technology, 44249 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, Kaunas University of Technology, 44249 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2809-2213","authenticated-orcid":false,"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, Kaunas University of Technology, 44249 Kaunas, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1561\/2000000071","article-title":"Deep learning in object recognition, detection, and segmentation","volume":"8","author":"Wang","year":"2016","journal-title":"Found. Trends Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Guzsvinecz, T., Szucs, V., and Sik-Lanyi, C. (2019). Suitability of the kinect sensor and leap motion controller\u2014A literature review. Sensors, 19.","DOI":"10.3390\/s19051072"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shires, L., Battersby, S., Lewis, J., Brown, D., Sherkat, N., and Standen, P. (2013, January 2\u20133). Enhancing the tracking capabilities of the Microsoft Kinect for stroke rehabilitation. Proceedings of the 2013 IEEE 2nd International Conference on Serious Games and Applications for Health (SeGAH), Vilamoura, Portugal.","DOI":"10.1109\/SeGAH.2013.6665316"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2630","DOI":"10.1049\/iet-ipr.2019.1622","article-title":"Depth map artefacts reduction: A review","volume":"14","author":"Ibrahim","year":"2020","journal-title":"IET Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103055","DOI":"10.1016\/j.jvcir.2021.103055","article-title":"Human pose estimation and its application to action recognition: A survey","volume":"76","author":"Song","year":"2021","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100338","DOI":"10.1016\/j.cosrev.2020.100338","article-title":"Real-time 3D reconstruction techniques applied in dynamic scenes: A systematic literature review","volume":"39","author":"Ingale","year":"2021","journal-title":"Comput. Sci. Rev."},{"key":"ref_7","unstructured":"Oved, D., and Zhu, T. (2022, January 20). BodyPix: Real-Time Person Segmentation in the Browser with TensorFlow.js. Available online: https:\/\/blog.tensorflow.org\/2019\/11\/updated-bodypix-2.html."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/3391743","article-title":"Video Object Segmentation and Tracking: A survey","volume":"11","author":"Yao","year":"2020","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_9","first-page":"253","article-title":"Gender detection using 3d anthropometric measurements by kinect","volume":"25","author":"Camalan","year":"2018","journal-title":"Metrol. Meas. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object Detection With Deep Learning: A Review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qiao, M., Cheng, J., Bian, W., and Tao, D. (2014). Biview learning for human posture segmentation from 3D points cloud. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085811"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1109\/TCYB.2013.2275945","article-title":"Real-time posture reconstruction for Microsoft Kinect","volume":"43","author":"Shum","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s13673-020-00256-4","article-title":"Multiple Kinect based system to monitor and analyze key performance indicators of physical training","volume":"10","author":"Ryselis","year":"2020","journal-title":"Hum.-Centric Comput. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.cviu.2015.12.011","article-title":"Improving posture classification accuracy for depth sensor-based human activity monitoring in smart environments","volume":"148","author":"Ho","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103010","DOI":"10.1016\/j.cviu.2020.103010","article-title":"High-speed multi-person pose estimation with deep feature transfer","volume":"197\u2013198","author":"Huang","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1007\/s11263-012-0557-0","article-title":"Using Segmented 3D Point Clouds for Accurate Likelihood Approximation in Human Pose Tracking","volume":"101","author":"Lehment","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e447","DOI":"10.7717\/peerj-cs.442","article-title":"Detection of sitting posture using hierarchical image composition and deep learning","volume":"7","author":"Kulikajevas","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1111\/cgf.14151","article-title":"PointSkelCNN: Deep Learning-Based 3D Human Skeleton Extraction from Point Clouds","volume":"39","author":"Qin","year":"2020","journal-title":"Comput. Graph. Forum"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"27757","DOI":"10.1109\/JSEN.2021.3124451","article-title":"Adversarial 3D Human Pointcloud Completion from Limited Angle Depth Data","volume":"21","author":"Kulikajevas","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kulikajevas, A., Maskeli\u016bnas, R., Dama\u0161evi\u010dius, R., and Wlodarczyk-Sielicka, M. (2021). Auto-refining reconstruction algorithm for recreation of limited angle humanoid depth data. Sensors, 21.","DOI":"10.3390\/s21113702"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kulikajevas, A., Maskeliunas, R., Damasevicius, R., and Scherer, R. (2021). Humannet-a two-tiered deep neural network architecture for self-occluding humanoid pose reconstruction. Sensors, 21.","DOI":"10.3390\/s21123945"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1109\/TMM.2021.3076340","article-title":"3DBodyNet: Fast Reconstruction of 3D Animatable Human Body Shape from a Single Commodity Depth Camera","volume":"24","author":"Hu","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_23","unstructured":"Google Developers (2022, January 10). Protocol Buffer Basics: Java. Available online: https:\/\/developers.google.com\/protocol-buffers\/docs\/javatutorial."},{"key":"ref_24","unstructured":"Tomassi, C., and Manduchi, R. (1998, January 7). Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271), Bombay, India."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1145\/361002.361007","article-title":"Multidimensional Binary Search Trees Used for Associative Searching","volume":"18","author":"Bentley","year":"1975","journal-title":"Commun. ACM"},{"key":"ref_26","unstructured":"Serkan, T. (2022, January 15). Euclidean Cluster Extraction-Point Cloud Library 0.0 Documentation. Available online: https:\/\/pcl.readthedocs.io\/en\/latest\/cluster_extraction.html."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/BF00263763","article-title":"Worst-case analysis for region and partial region searches in multidimensional binary search trees and balanced quad trees","volume":"9","author":"Lee","year":"1977","journal-title":"Acta Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s11263-016-0901-x","article-title":"Multi-modal rgb\u2013depth\u2013thermal human body segmentation","volume":"118","author":"Palmero","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.neucom.2013.03.003","article-title":"Robust human body segmentation based on part appearance and spatial constraint","volume":"118","author":"Huang","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3402","DOI":"10.1016\/j.patcog.2012.03.011","article-title":"Arbitrary body segmentation in static images","volume":"45","author":"Li","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_31","unstructured":"Couprie, C., Farabet, C., Najman, L., and LeCun, Y. (2013). Indoor semantic segmentation using depth information. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, W., and Neumann, U. (2018, January 8\u201314). Depth-aware cnn for rgb-d segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_9"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3531\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:06:53Z","timestamp":1760137613000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3531"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,6]]},"references-count":32,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093531"],"URL":"https:\/\/doi.org\/10.3390\/s22093531","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,5,6]]}}}