{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T03:46:46Z","timestamp":1761709606726,"version":"build-2065373602"},"reference-count":70,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,10]],"date-time":"2019-08-10T00:00:00Z","timestamp":1565395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001866","name":"Fonds National de la Recherche Luxembourg","doi-asserted-by":"publisher","award":["C15\/IS\/10415355\/3DACT\/Bj\u00f6rn Ottersten."],"award-info":[{"award-number":["C15\/IS\/10415355\/3DACT\/Bj\u00f6rn Ottersten."]}],"id":[{"id":"10.13039\/501100001866","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides an advanced discriminative representation of actions. Moreover, we generalize Localized Trajectories to 3D by using the depth modality. One of the main advantages of 3D Localized Trajectories is that they describe radial displacements that are perpendicular to the image plane. Extensive experiments and analysis were carried out on five different datasets.<\/jats:p>","DOI":"10.3390\/s19163503","type":"journal-article","created":{"date-parts":[[2019,8,12]],"date-time":"2019-08-12T06:38:02Z","timestamp":1565591882000},"page":"3503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Localized Trajectories for 2D and 3D Action Recognition"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1173-6318","authenticated-orcid":false,"given":"Konstantinos","family":"Papadopoulos","sequence":"first","affiliation":[{"name":"Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, L-1855 Luxembourg, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Girum","family":"Demisse","sequence":"additional","affiliation":[{"name":"Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, L-1855 Luxembourg, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enjie","family":"Ghorbel","sequence":"additional","affiliation":[{"name":"Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, L-1855 Luxembourg, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michel","family":"Antunes","sequence":"additional","affiliation":[{"name":"Perceive3D, 3030-199 Coimbra, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Djamila","family":"Aouada","sequence":"additional","affiliation":[{"name":"Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, L-1855 Luxembourg, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bj\u00f6rn","family":"Ottersten","sequence":"additional","affiliation":[{"name":"Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, L-1855 Luxembourg, Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,10]]},"reference":[{"key":"ref_1","first-page":"380","article-title":"Anticipating Suspicious Actions using a Small Dataset of Action Templates","volume":"Volume 5","author":"Baptista","year":"2018","journal-title":"Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications"},{"doi-asserted-by":"crossref","unstructured":"Baptista, R., Antunes, M., Shabayek, A.E.R., Aouada, D., and Ottersten, B. (2017, January 21\u201323). Flexible feedback system for posture monitoring and correction. Proceedings of the 2017 Fourth International Conference on Image Information Processing (ICIIP), Waknaghat, India.","key":"ref_2","DOI":"10.1109\/ICIIP.2017.8313687"},{"key":"ref_3","first-page":"274","article-title":"Video-based Feedback for Assisting Physical Activity","volume":"Volume 5","author":"Baptista","year":"2017","journal-title":"Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/2133366.2133371","article-title":"Continuous Body and Hand Gesture Recognition for Natural Human-computer Interaction","volume":"2","author":"Song","year":"2012","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.cviu.2006.07.013","article-title":"Free viewpoint action recognition using motion history volumes","volume":"104","author":"Weinland","year":"2006","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/34.910878","article-title":"The recognition of human movement using temporal templates","volume":"23","author":"Bobick","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Wang, H., Kl\u00e4ser, A., Schmid, C., and Liu, C. (2011, January 20\u201325). Action recognition by dense trajectories. Proceedings of the CVPR 2011, Providence, RI, USA.","key":"ref_7","DOI":"10.1109\/CVPR.2011.5995407"},{"unstructured":"Li, F.F., and Perona, P. (2005, January 20\u201325). A Bayesian Hierarchical Model for Learning Natural Scene Categories. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA.","key":"ref_8"},{"doi-asserted-by":"crossref","unstructured":"Koperski, M., Bilinski, P., and Bremond, F. (2014, January 27\u201330). 3D trajectories for action recognition. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","key":"ref_9","DOI":"10.1109\/ICIP.2014.7025848"},{"unstructured":"Lazebnik, S., Schmid, C., and Ponce, J. (2006, January 17\u201322). Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR \u201906), New York, NY, USA.","key":"ref_10"},{"unstructured":"Wang, J., Liu, Z., Wu, Y., and Yuan, J. (2012, January 16\u201321). Mining actionlet ensemble for action recognition with depth cameras. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","key":"ref_11"},{"doi-asserted-by":"crossref","unstructured":"Papadopoulos, K., Antunes, M., Aouada, D., and Ottersten, B. (2017, January 17\u201320). Enhanced trajectory-based action recognition using human pose. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","key":"ref_12","DOI":"10.1109\/ICIP.2017.8296593"},{"doi-asserted-by":"crossref","unstructured":"Wang, H., and Schmid, C. (2013, January 1\u20138). Action Recognition with Improved Trajectories. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","key":"ref_13","DOI":"10.1109\/ICCV.2013.441"},{"doi-asserted-by":"crossref","unstructured":"Wang, L., Qiao, Y., and Tang, X. (2015, January 7\u201312). Action recognition with trajectory-pooled deep-convolutional descriptors. Proceedings of the2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","key":"ref_14","DOI":"10.1109\/CVPR.2015.7299059"},{"key":"ref_15","first-page":"425","article-title":"Trajectory-Based Modeling of Human Actions with Motion Reference Points","volume":"Volume Part V","author":"Jiang","year":"2012","journal-title":"Proceedings of the 12th European Conference on Computer Vision, ECCV \u201912"},{"doi-asserted-by":"crossref","unstructured":"Ni, B., Moulin, P., Yang, X., and Yan, S. (2015, January 7\u201312). Motion Part Regularization: Improving action recognition via trajectory group selection. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","key":"ref_16","DOI":"10.1109\/CVPR.2015.7298993"},{"unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA.","key":"ref_17"},{"doi-asserted-by":"crossref","unstructured":"Chaudhry, R., Ravichandran, A., Hager, G., and Vidal, R. (2008, January 20\u201325). Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","key":"ref_18","DOI":"10.1109\/CVPRW.2009.5206821"},{"doi-asserted-by":"crossref","unstructured":"Jhuang, H., Gall, J., Zuffi, S., Schmid, C., and Black, M.J. (2013, January 1\u20138). Towards Understanding Action Recognition. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","key":"ref_19","DOI":"10.1109\/ICCV.2013.396"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.imavis.2016.06.007","article-title":"From Handcrafted to Learned Representations for Human Action Recognition","volume":"55","author":"Zhu","year":"2016","journal-title":"Image Vis. Comput."},{"doi-asserted-by":"crossref","unstructured":"Ke, Q., Bennamoun, M., An, S., Sohel, F., and Boussaid, F. (2017, January 21\u201326). A New Representation of Skeleton Sequences for 3D Action Recognition. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_21","DOI":"10.1109\/CVPR.2017.486"},{"doi-asserted-by":"crossref","unstructured":"Song, S., Lan, C., Xing, J., Zeng, W., and Liu, J. (2017, January 4\u20139). An End-to-end Spatio-temporal Attention Model for Human Action Recognition from Skeleton Data. Proceedings of the thirty-First AAAI Conference on Artificial Intelligence, AAAI \u201917, San Francisco, CA, USA.","key":"ref_22","DOI":"10.1609\/aaai.v31i1.11212"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1109\/LSP.2017.2690339","article-title":"SkeletonNet: Mining Deep Part Features for 3-D Action Recognition","volume":"24","author":"Ke","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"doi-asserted-by":"crossref","unstructured":"Oreifej, O., and Liu, Z. (2013, January 23\u201328). HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","key":"ref_24","DOI":"10.1109\/CVPR.2013.98"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1007\/978-3-642-33709-3_62","article-title":"Robust 3D Action Recognition with Random Occupancy Patterns","volume":"Volume 7573","author":"Wang","year":"2012","journal-title":"Proceedings of the 12th European Conference on Computer Vision\u2014ECCV 2012"},{"doi-asserted-by":"crossref","unstructured":"Xia, L., and Aggarwal, J.K. (2013, January 23\u201328). Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","key":"ref_26","DOI":"10.1109\/CVPR.2013.365"},{"doi-asserted-by":"crossref","unstructured":"Klaeser, A., Marszalek, M., and Schmid, C. (2008, January 1\u20134). A Spatio-Temporal Descriptor Based on 3D-Gradients. Proceedings of the British Machine Vision Conference, Leeds, UK.","key":"ref_27","DOI":"10.5244\/C.22.99"},{"doi-asserted-by":"crossref","unstructured":"Ohn-Bar, E., and Trivedi, M.M. (2013, January 23\u201328). Joint Angles Similarities and HOG2 for Action Recognition. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, USA.","key":"ref_28","DOI":"10.1109\/CVPRW.2013.76"},{"doi-asserted-by":"crossref","unstructured":"Foggia, P., Percannella, G., Saggese, A., and Vento, M. (2013, January 13\u201316). Recognizing Human Actions by a Bag of Visual Words. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK.","key":"ref_29","DOI":"10.1109\/SMC.2013.496"},{"unstructured":"Shukla, P., Biswas, K.K., and Kalra, P.K. (2013, January 20\u201323). Action Recognition using Temporal Bag-of-Words from Depth Maps. Proceedings of the IEEE International Conference on Machine Vision Applications, Kyoto, Japan.","key":"ref_30"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/TPAMI.2016.2565479","article-title":"Super Normal Vector for Human Activity Recognition with Depth Cameras","volume":"39","author":"Yang","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Slama, R., Wannous, H., and Daoudi, M. (2014, January 24\u201328). Grassmannian Representation of Motion Depth for 3D Human Gesture and Action Recognition. Proceedings of the 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","key":"ref_32","DOI":"10.1109\/ICPR.2014.602"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1109\/TPAMI.2016.2533389","article-title":"Histogram of Oriented Principal Components for Cross-View Action Recognition","volume":"38","author":"Rahmani","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Zanfir, M., Leordeanu, M., and Sminchisescu, C. (2013, January 1\u20138). The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","key":"ref_34","DOI":"10.1109\/ICCV.2013.342"},{"doi-asserted-by":"crossref","unstructured":"Yang, X., and Tian, Y.L. (2012, January 16\u201321). EigenJoints-based action recognition using Na\u00efve-Bayes-Nearest-Neighbor. Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","key":"ref_35","DOI":"10.1109\/CVPRW.2012.6239232"},{"doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Arrate, F., and Chellappa, R. (2014, January 23\u201328). Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","key":"ref_36","DOI":"10.1109\/CVPR.2014.82"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1109\/TCYB.2014.2350774","article-title":"3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold","volume":"45","author":"Devanne","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TPAMI.2015.2439257","article-title":"Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories","volume":"38","author":"Amor","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Demisse, G.G., Papadopoulos, K., Aouada, D., and Ottersten, B. (2018, January 18\u201322). Pose Encoding for Robust Skeleton-Based Action Recognition. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","key":"ref_39","DOI":"10.1109\/CVPRW.2018.00056"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.imavis.2018.06.004","article-title":"Kinematic Spline Curves: A temporal invariant descriptor for fast action recognition","volume":"77","author":"Ghorbel","year":"2018","journal-title":"Image Vis. Comput."},{"doi-asserted-by":"crossref","unstructured":"Li, W., Zhang, Z., and Liu, Z. (2010, January 13\u201318). Action recognition based on a bag of 3D points. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition\u2014Workshops, San Francisco, CA, USA.","key":"ref_41","DOI":"10.1109\/CVPRW.2010.5543273"},{"unstructured":"Farneb\u00e4ck, G. (July, January 29). Two-frame Motion Estimation Based on Polynomial Expansion. Proceedings of the 13th Scandinavian Conference on Image Analysis, SCIA \u201903, Halmstad, Sweden.","key":"ref_42"},{"doi-asserted-by":"crossref","unstructured":"Raptis, M., Kokkinos, I., and Soatto, S. (2012, January 16\u201321). Discovering discriminative action parts from mid-level video representations. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","key":"ref_43","DOI":"10.1109\/CVPR.2012.6247807"},{"doi-asserted-by":"crossref","unstructured":"Jaimez, M., Souiai, M., Gonzalez-Jimenez, J., and Cremers, D. (2015, January 16\u201330). A primal-dual framework for real-time dense RGB-D scene flow. Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","key":"ref_44","DOI":"10.1109\/ICRA.2015.7138986"},{"doi-asserted-by":"crossref","unstructured":"Quiroga, J., Brox, T., Devernay, F., and Crowley, J.L. (2014, January 6\u201312). Dense Semi-Rigid Scene Flow Estimation from RGBD images. Proceedings of the ECCV 2014\u2014European Conference on Computer Vision, Zurich, Switzerland.","key":"ref_45","DOI":"10.1007\/978-3-319-10584-0_37"},{"doi-asserted-by":"crossref","unstructured":"Sun, D., Sudderth, E.B., and Pfister, H. (2015, January 7\u201312). Layered RGBD scene flow estimation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","key":"ref_46","DOI":"10.1109\/CVPR.2015.7298653"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1109\/TPAMI.2016.2622698","article-title":"Real-Time Enhancement of Dynamic Depth Videos with Non-Rigid Deformations","volume":"39","author":"Aouada","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.cviu.2016.04.006","article-title":"Enhancement of dynamic depth scenes by upsampling for precise super-resolution (UP-SR)","volume":"147","author":"Aouada","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/JSTSP.2012.2193556","article-title":"A Local 3-D Motion Descriptor for Multi-View Human Action Recognition from 4-D Spatio-Temporal Interest Points","volume":"6","author":"Holte","year":"2012","journal-title":"IEEE J. Sel. Top. Signal Process."},{"unstructured":"Cremers, D., Reid, I., Saito, H., and Yang, M.H. (2015). Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction. Computer Vision\u2014ACCV 2014, Springer International Publishing.","key":"ref_50"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.cviu.2015.12.001","article-title":"Hierarchical Transfer Learning for Online Recognition of Compound Actions","volume":"144","author":"Bloom","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1109\/TPAMI.2017.2679054","article-title":"Watch-n-Patch: Unsupervised Learning of Actions and Relations","volume":"40","author":"Wu","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/THMS.2014.2377111","article-title":"Human Activity Recognition Process Using 3-D Posture Data","volume":"45","author":"Gaglio","year":"2015","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"unstructured":"M\u00fcller, M., and R\u00f6der, T. (2006, January 2\u20134). Motion Templates for Automatic Classification and Retrieval of Motion Capture Data. Proceedings of the 2006 ACM SIGGRAPH\/Eurographics Symposium on Computer Animation (SCA \u201906), Vienna, Austria.","key":"ref_54"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.jvcir.2014.11.008","article-title":"TriViews: A general framework to use 3D depth data effectively for action recognition","volume":"26","author":"Chen","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"doi-asserted-by":"crossref","unstructured":"Luo, Z., Peng, B., Huang, D., Alahi, A., and Fei-Fei, L. (2017, January 21\u201326). Unsupervised Learning of Long-Term Motion Dynamics for Videos. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_56","DOI":"10.1109\/CVPR.2017.751"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.patcog.2016.08.003","article-title":"Robust Human Activity Recognition from Depth Video Using Spatiotemporal Multi-fused Features","volume":"61","author":"Jalal","year":"2017","journal-title":"Pattern Recogn."},{"unstructured":"Campilho, A., and Kamel, M. (2014). Exemplar-Based Human Action Recognition with Template Matching from a Stream of Motion Capture. Image Analysis and Recognition, Springer International Publishing.","key":"ref_58"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s00500-014-1237-5","article-title":"Motion Retrieval Using Weighted Graph Matching","volume":"19","author":"Xiao","year":"2015","journal-title":"Soft Comput."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.cag.2015.07.005","article-title":"3D human motion retrieval using graph kernels based on adaptive graph construction","volume":"54","author":"Li","year":"2016","journal-title":"Comput. Graph."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.patcog.2013.06.020","article-title":"Ongoing human action recognition with motion capture","volume":"47","author":"Barnachon","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.patrec.2014.06.005","article-title":"Activity-based methods for person recognition in motion capture sequences","volume":"49","author":"Fotiadou","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"489","DOI":"10.14419\/ijet.v7i1.1.10152","article-title":"Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels","volume":"7","author":"Kishore","year":"2018","journal-title":"Int. J. Eng. Technol."},{"doi-asserted-by":"crossref","unstructured":"Huang, Z., Wan, C., Probst, T., and Gool, L.V. (2017, January 21\u201326). Deep Learning on Lie Groups for Skeleton-Based Action Recognition. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_64","DOI":"10.1109\/CVPR.2017.137"},{"doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., and Chellappa, R. (2016, January 27\u201330). Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","key":"ref_65","DOI":"10.1109\/CVPR.2016.484"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.patcog.2016.07.041","article-title":"Motion segment decomposition of RGB-D sequences for human behavior understanding","volume":"61","author":"Devanne","year":"2017","journal-title":"Pattern Recognit."},{"doi-asserted-by":"crossref","unstructured":"Ahmed, F., Paul, P.P., and Gavrilova, M.L. (2016, January 23\u201325). Joint-Triplet Motion Image and Local Binary Pattern for 3D Action Recognition Using Kinect. Proceedings of the 29th International Conference on Computer Animation and Social Agents, CASA \u201916, Geneva, Switzerland.","key":"ref_67","DOI":"10.1145\/2915926.2915937"},{"unstructured":"Tian, Y., Kanade, T., and Cohn, J.F. (2002, January 21). Evaluation of Gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity. Proceedings of the Fifth IEEE International Conference on Automatic Face Gesture Recognition, Washington, DC, USA.","key":"ref_68"},{"doi-asserted-by":"crossref","unstructured":"Xia, L., Chen, C., and Aggarwal, J.K. (2012, January 16\u201321). View invariant human action recognition using histograms of 3D joints. Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","key":"ref_69","DOI":"10.1109\/CVPRW.2012.6239233"},{"doi-asserted-by":"crossref","unstructured":"Varrette, S., Bouvry, P., Cartiaux, H., and Georgatos, F. (2014, January 21\u201325). Management of an academic HPC cluster: The UL experience. Proceedings of the 2014 International Conference on High Performance Computing Simulation (HPCS), Bologna, Italy.","key":"ref_70","DOI":"10.1109\/HPCSim.2014.6903792"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3503\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:10:12Z","timestamp":1760188212000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/16\/3503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,10]]},"references-count":70,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["s19163503"],"URL":"https:\/\/doi.org\/10.3390\/s19163503","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,8,10]]}}}