{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:03:04Z","timestamp":1743138184495,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031064326"},{"type":"electronic","value":"9783031064333"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06433-3_15","type":"book-chapter","created":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T18:03:24Z","timestamp":1652551404000},"page":"170-181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FirstPiano: A New Egocentric Hand Action Dataset Oriented Towards Augmented Reality Applications"],"prefix":"10.1007","author":[{"given":"Th\u00e9o","family":"Voillemin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hazem","family":"Wannous","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Philippe","family":"Vandeborre","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,15]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Bambach, S., Lee, S., Crandall, D.J., Yu, C.: Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions. In: IEEE International Conference on Computer Vision (ICCV). pp. 1949\u20131957 (2015)","DOI":"10.1109\/ICCV.2015.226"},{"issue":"3","key":"15_CR2","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1177\/0278364914555720","volume":"34","author":"IM Bullock","year":"2015","unstructured":"Bullock, I.M., Feix, T., Dollar, A.M.: The yale human grasping dataset: Grasp, object, and task data in household and machine shop environments. The International Journal of Robotics Research 34(3), 251\u2013255 (2015)","journal-title":"The International Journal of Robotics Research"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Cai, M., Kitani, K.M., Sato, Y.: A scalable approach for understanding the visual structures of hand grasps. In: IEEE International Conference on Robotics and Automation (ICRA). pp. 1360\u20131366 (2015)","DOI":"10.1109\/ICRA.2015.7139367"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Guo, H., Wang, G., Zhang, L.: Motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. IEEE International Conference on Image Processing (ICIP), September 2017","DOI":"10.1109\/ICIP.2017.8296809"},{"key":"15_CR5","unstructured":"De Smedt, Q., Wannous, H., Vandeborre, J.P., Guerry, J., Saux, B.L., Filliat, D.: 3D hand gesture recognition using a depth and skeletal dataset: Shrec 2017 track. In: Proceedings of the Workshop on 3D Object Retrieval. 3Dor 2017, pp. 33\u201338. Eurographics Association, Goslar, DEU (2017)"},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"De Smedt, Q., Wannous, H., Vandeborre, J.-P.: 3D hand gesture recognition by analysing set-of-joints trajectories. In: Wannous, H., Pala, P., Daoudi, M., Fl\u00f3rez-Revuelta, F. (eds.) UHA3DS 2016. LNCS, vol. 10188, pp. 86\u201397. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-91863-1_7","DOI":"10.1007\/978-3-319-91863-1_7"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Devanne, M., Wannous, H., Daoudi, M., Berretti, S., Bimbo, A.D., Pala, P.: Learning Shape Variations of Motion Trajectories for Gait Analysis. In: International Conference on Pattern Recognition (ICPR). pp. 895\u2013900. Cancun, Mexico (2016)","DOI":"10.1109\/ICPR.2016.7899749"},{"key":"15_CR8","unstructured":"Duarte, K., Rawat, Y., Shah, M.: VideoCapsuleNet : a simplified network for action detection. In: Advances in Neural Information Processing Systems, pp. 7610\u20137619 (2018)"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Essig, K., Strenge, B., Schack, T.: ADAMAAS: towards smart glasses for mobile and personalized action assistance.. In: 9th ACM International Conference, pp. 1\u20134, June 2016","DOI":"10.1145\/2910674.2910727"},{"key":"15_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-030-58539-6_8","volume-title":"Computer Vision \u2013 ECCV 2020","author":"L Fang","year":"2020","unstructured":"Fang, L., Liu, X., Liu, L., Xu, H., Kang, W.: JGR-P2O: joint graph reasoning based pixel-to-offset prediction network for 3D hand pose estimation from a single depth image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 120\u2013137. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_8"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Fathi, A., Ren, X., Rehg, J.M.: Learning to recognize objects in egocentric activities. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3281\u20133288 (2011)","DOI":"10.1109\/CVPR.2011.5995444"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Garcia-Hernando, G., Yuan, S., Baek, S., Kim, T.K.: First-person hand action benchmark with RGB-D videos and 3D hand pose annotations. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 409\u2013419 (2018)","DOI":"10.1109\/CVPR.2018.00050"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Goyal, R., et al.: The something video database for learning and evaluating visual common sense. In: IEEE International Conference on Computer Vision (ICCV) 2017, pp. 5843\u20135851. Los Alamitos, CA, USA, October 2017","DOI":"10.1109\/ICCV.2017.622"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725\u20131732 (2014)","DOI":"10.1109\/CVPR.2014.223"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Sharif, M., Akram, T., Raza, M., Saba, T., Rehman, A.: Hand-crafted and deep convolutional neural network features fusion and selection strategy: an application to intelligent human action recognition. Appl. Soft Comput. 87, 105986 (2020)","DOI":"10.1016\/j.asoc.2019.105986"},{"key":"15_CR16","unstructured":"Li, C., Li, S., Gao, Y., Zhang, X., Li, W.: A two-stream neural network for pose-based hand gesture recognition. CoRR abs\/2101.08926 (2021)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, M., Rehg, J.M.: In the eye of beholder: joint learning of gaze and actions in first person video. In: Proceedings of the European Conference on Computer Vision (ECCV), September 2018","DOI":"10.1007\/978-3-030-01228-1_38"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Lin, J., Gan, C., Han, S.: Tsm: Temporal shift module for efficient video understanding. In: IEEE International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00718"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Moghimi, M., Azagra, P., Montesano, L., Murillo, A.C., Belongie, S.: Experiments on an RGB-D wearable vision system for egocentric activity recognition. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 611\u2013617 (2014)","DOI":"10.1109\/CVPRW.2014.94"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., Kautz, J.: Online detection and classification of dynamic hand gestures with recurrent 3D convolutional neural network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4207\u20134215 (2016)","DOI":"10.1109\/CVPR.2016.456"},{"key":"15_CR21","unstructured":"Oberweger, M., Wohlhart, P., Lepetit, V.: Hands deep in deep learning for hand pose estimation. In: Computer Vision Winter Workshop, pp. 1\u201310 (2015)"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Pirsiavash, H., Ramanan, D.: Detecting activities of daily living in first-person camera views. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2847\u20132854 (2012)","DOI":"10.1109\/CVPR.2012.6248010"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Rajasegaran, J., Jayasundara, V., Jayasekara, S., Jayasekara, H., Seneviratne, S., Rodrigo, R.: DeepCaps: going deeper with capsule networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10717\u201310725 (2019)","DOI":"10.1109\/CVPR.2019.01098"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Rhif, M., Wannous, H., Farah, I.R.: Action recognition from 3D skeleton sequences using deep networks on lie group features. In: 24th International Conference on Pattern Recognition (ICPR), pp. 3427\u20133432 (2018)","DOI":"10.1109\/ICPR.2018.8546027"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Rogez, G., Supancic, J.S., Ramanan, D.: Understanding everyday hands in action from RGB-D images. In: IEEE International Conference on Computer Vision (ICCV), pp. 3889\u20133897 (2015)","DOI":"10.1109\/ICCV.2015.443"},{"key":"15_CR26","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. Red Hook (2017)"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Schr\u00f6der, M., Ritter, H.: Deep learning for action recognition in augmented reality assistance systems. In: ACM SIGGRAPH 2017 Posters, pp. 1\u20132, June 2017","DOI":"10.1145\/3102163.3102191"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Tang, Y., Tian, Y., Lu, J., Feng, J., Zhou, J.: Action recognition in RGB-D egocentric videos. In: IEEE International Conference on Image Processing (ICIP), pp. 3410\u20133414 (2017)","DOI":"10.1109\/ICIP.2017.8296915"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Voillemin, T., Wannous, H., Vandeborre, J.P.: 2D deep video capsule network with temporal shift for action recognition. In: 25th International Conference on Pattern Recognition (ICPR), pp. 3513\u20133519 (2021)","DOI":"10.1109\/ICPR48806.2021.9412983"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4305\u20134314 (2015)","DOI":"10.1109\/CVPR.2015.7299059"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Wang, S., Hou, Y., Li, Z., Dong, J., Tang, C.: Combining convnets with hand-crafted features for action recognition based on an HMM-SVM classifier. Multim. Tools Appl. 77(15), 18983\u201318998 (2018)","DOI":"10.1007\/s11042-017-5335-0"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06433-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T20:31:17Z","timestamp":1727209877000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06433-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064326","9783031064333"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06433-3_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"168","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"55% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}