{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:12:18Z","timestamp":1743145938030,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819916412"},{"type":"electronic","value":"9789819916429"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-1642-9_45","type":"book-chapter","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T12:14:57Z","timestamp":1681388097000},"page":"528-539","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards Human Keypoint Detection in\u00a0Infrared Images"],"prefix":"10.1007","author":[{"given":"Zhilei","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Wanli","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Xiaoming","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Anjie","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Yuqin","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"45_CR1","doi-asserted-by":"crossref","unstructured":"Baradel, F., Neverova, N., Wolf, C., Mille, J., Mori, G.: Object level visual reasoning in videos. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 105\u2013121 (2018)","DOI":"10.1007\/978-3-030-01261-8_7"},{"key":"45_CR2","doi-asserted-by":"crossref","unstructured":"Mazhar, O., Ramdani, S., Navarro, B., Passama, R., Cherubini, A.: Towards real-time physical human-robot interaction using skeleton information and hand gestures. In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/IROS.2018.8594385"},{"issue":"9","key":"45_CR3","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1007\/s11263-018-1077-3","volume":"126","author":"H Hattori","year":"2018","unstructured":"Hattori, H., Lee, N., Boddeti, V.N., Beainy, F., Kitani, K.M., Kanade, T.: Synthesizing a scene-specific pedestrian detector and pose estimator for static video surveillance. Int. J. Comput. Vision 126(9), 1027\u20131044 (2018)","journal-title":"Int. J. Comput. Vision"},{"issue":"9","key":"45_CR4","doi-asserted-by":"publisher","first-page":"2639","DOI":"10.1007\/s11263-021-01482-8","volume":"129","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Chen, Z., Tao, D.: Towards high performance human keypoint detection. Int. J. Comput. Vision 129(9), 2639\u20132662 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"45_CR5","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.neucom.2020.12.090","volume":"436","author":"T Liu","year":"2021","unstructured":"Liu, T., Wang, J., Yang, B., Wang, X.: Ngdnet: nonuniform gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom. Neurocomputing 436, 210\u2013220 (2021)","journal-title":"Neurocomputing"},{"issue":"1","key":"45_CR6","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TITS.2016.2569159","volume":"18","author":"J-Y Kwak","year":"2016","unstructured":"Kwak, J.-Y., Ko, B.C., Nam, J.Y.: Pedestrian tracking using online boosted random ferns learning in far-infrared imagery for safe driving at night. IEEE Trans. Intell. Transp. Syst. 18(1), 69\u201381 (2016)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"45_CR7","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.cogsys.2018.04.002","volume":"50","author":"A Akula","year":"2018","unstructured":"Akula, A., Shah, A.K., Ghosh, R.: Deep learning approach for human action recognition in infrared images. Cogn. Syst. Res. 50, 146\u2013154 (2018)","journal-title":"Cogn. Syst. Res."},{"key":"45_CR8","doi-asserted-by":"crossref","unstructured":"Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 466\u2013481 (2018)","DOI":"10.1007\/978-3-030-01231-1_29"},{"key":"45_CR9","doi-asserted-by":"crossref","unstructured":"Toshev, A., Szegedy, C., Deeppose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2014)","DOI":"10.1109\/CVPR.2014.214"},{"key":"45_CR10","doi-asserted-by":"crossref","unstructured":"Pfister, T., Charles, J., Zisserman, A.: Flowing convnets for human pose estimation in videos. In: Proceedings of the IEEE International Conference on Computer Vision, ICCV (2015)","DOI":"10.1109\/ICCV.2015.222"},{"key":"45_CR11","doi-asserted-by":"crossref","unstructured":"Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2016)","DOI":"10.1109\/CVPR.2016.511"},{"key":"45_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-3-319-46484-8_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Newell","year":"2016","unstructured":"Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483\u2013499. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_29"},{"key":"45_CR13","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2017)","DOI":"10.1109\/CVPR.2017.143"},{"key":"45_CR14","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J.: Cascaded pyramid network for multi-person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2018)","DOI":"10.1109\/CVPR.2018.00742"},{"key":"45_CR15","unstructured":"Li, W.: Rethinking on multi-stage networks for human pose estimation (2019). 10.48550\/ARXIV.1901.00148, arxiv.org\/abs\/1901.00148"},{"key":"45_CR16","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR (2019)","DOI":"10.1109\/CVPR.2019.00584"},{"key":"45_CR17","doi-asserted-by":"crossref","unstructured":"Kreiss, S., Bertoni, L., Alahi, A.: Pifpaf: composite fields for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR (2019)","DOI":"10.1109\/CVPR.2019.01225"},{"key":"45_CR18","unstructured":"Cheng, B., Xiao, B., Wang, J., Shi, H., Huang, T.S., Zhang, L.: Bottomup higher-resolution networks for multi-person pose estimation. arXiv preprint arXiv:1908.10357 (2019)"},{"key":"45_CR19","doi-asserted-by":"crossref","unstructured":"Govardhan, P., Pati, U.C.: Nir image based pedestrian detection in night vision with cascade classification and validation. In: 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1435\u20131438. IEEE (2014)","DOI":"10.1109\/ICACCCT.2014.7019339"},{"issue":"6","key":"45_CR20","doi-asserted-by":"publisher","first-page":"1368","DOI":"10.1109\/TCSVT.2016.2539684","volume":"27","author":"M Jeong","year":"2016","unstructured":"Jeong, M., Ko, B.C., Nam, J.-Y.: Early detection of sudden pedestrian crossing for safe driving during summer nights. IEEE Trans. Circuits Syst. Video Technol. 27(6), 1368\u20131380 (2016)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"45_CR21","doi-asserted-by":"crossref","unstructured":"Heo, D., Lee, E., Ko, B.C.: Pedestrian detection at night using deep neural networks and saliency maps. Electron. Imaging 2018(17), 60403-1 (2018)","DOI":"10.2352\/J.ImagingSci.Technol.2017.61.6.060403"},{"key":"45_CR22","unstructured":"Herrmann, C., Ruf, M., Beyerer, J.: Cnn-based thermal infrared person detection by domain adaptation. In: Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything, vol. 10643. International Society for Optics and Photonics, p. 1064308 (2018)"},{"key":"45_CR23","doi-asserted-by":"publisher","first-page":"135023","DOI":"10.1109\/ACCESS.2019.2932749","volume":"7","author":"Z Cao","year":"2019","unstructured":"Cao, Z., Yang, H., Zhao, J., Pan, X., Zhang, L., Liu, Z.: A new region proposal network for far-infrared pedestrian detection. IEEE Access 7, 135023\u2013135030 (2019)","journal-title":"IEEE Access"},{"key":"45_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"45_CR25","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u2019ar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"issue":"8","key":"45_CR26","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1007\/s11760-021-01916-3","volume":"15","author":"Y Zang","year":"2021","unstructured":"Zang, Y., Fan, C., Zheng, Z., Yang, D.: Pose estimation at night in infrared images using a lightweight multi-stage attention network. SIViP 15(8), 1757\u20131765 (2021). https:\/\/doi.org\/10.1007\/s11760-021-01916-3","journal-title":"SIViP"},{"key":"45_CR27","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, realtime object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"45_CR28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"45_CR29","unstructured":"Veit, A., Wilber, M.J., Belongie, S.: Residual networks behave like ensembles of relatively shallow networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"45_CR30","unstructured":"Zhao, L., Wang, J., Li, X., Tu, Z., Zeng, W.: On the connection of deep fusion to ensembling. arXiv preprint arXiv:1611.07718 (2016)"},{"key":"45_CR31","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","volume":"90","author":"Z Wu","year":"2019","unstructured":"Wu, Z., Shen, C., Van Den Hengel, A.: Wider or deeper: revisiting the resnet model for visual recognition. Pattern Recogn. 90, 119\u2013133 (2019)","journal-title":"Pattern Recogn."},{"key":"45_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy, C.: Rabinovich, going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"45_CR33","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"45_CR34","unstructured":"Andrew, G., Menglong, Z., et al.: Efficient convolutional neural networks for mobile vision applications (2017)"},{"key":"45_CR35","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-1642-9_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T12:33:26Z","timestamp":1681389206000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-1642-9_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819916412","9789819916429"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-1642-9_45","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"44% - 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":"2.65","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":"3","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)"}},{"value":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}