{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:44:25Z","timestamp":1772822665705,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819947416","type":"print"},{"value":"9789819947423","type":"electronic"}],"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\/978-981-99-4742-3_34","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T00:02:38Z","timestamp":1690675358000},"page":"413-425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DisGait: A Prior Work of Gait Recognition Concerning Disguised Appearance and Pose"],"prefix":"10.1007","author":[{"given":"Shouwang","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiqi","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shichao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"34_CR1","doi-asserted-by":"crossref","unstructured":"Chao, H., He, Y., Zhang, J., Feng, J.: Gaitset: regarding gait as a set for cross-view gait recognition (2018)","DOI":"10.1609\/aaai.v33i01.33018126"},{"issue":"1","key":"34_CR2","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.jvcir.2013.02.006","volume":"25","author":"M Hofmann","year":"2014","unstructured":"Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., Rigoll, G.: The tum gait from audio, image and depth (gaid) database: multimodal recognition of subjects and traits. J. Vis. Commun. Image Represent. 25(1), 195\u2013206 (2014)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"34_CR3","unstructured":"Hofmann, M., Sural, S., Rigoll, G.: Gait recognition in the presence of occlusion: a new dataset and baseline algorithms (2011)"},{"key":"34_CR4","doi-asserted-by":"crossref","unstructured":"Huang, X., et al.: Context-sensitive temporal feature learning for gait recognition. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 12889\u201312898 (2021)","DOI":"10.1109\/ICCV48922.2021.01267"},{"issue":"5","key":"34_CR5","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1109\/TIFS.2012.2204253","volume":"7","author":"H Iwama","year":"2012","unstructured":"Iwama, H., Okumura, M., Makihara, Y., Yagi, Y.: The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans. Inf. Forensics Secur. 7(5), 1511\u20131521 (2012)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936\u2013944 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Makihara, Y., Nixon, M.S., Yagi, Y.: Gait recognition: databases, representations, and applications, pp. 1\u201313. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-03243-2_883-1"},{"issue":"2","key":"34_CR9","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1109\/TIFS.2011.2176118","volume":"7","author":"D Matovski","year":"2012","unstructured":"Matovski, D., Nixon, M., Mahmoodi, S., Carter, J.: The effect of time on gait recognition performance. IEEE Trans. Inf. Forensics Secur. 7(2), 543\u2013552 (2012)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Mu, Z., et al.: Resgait: the real-scene gait dataset. In: 2021 IEEE International Joint Conference on Biometrics (IJCB), pp. 1\u20138 (2021)","DOI":"10.1109\/IJCB52358.2021.9484347"},{"key":"34_CR11","unstructured":"Rafi, M., Raviraja, S., Wahidabanu, R.: Gait recognition: a biometric for security, October 2009"},{"issue":"2","key":"34_CR12","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TPAMI.2005.39","volume":"27","author":"S Sarkar","year":"2005","unstructured":"Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162\u2013177 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"34_CR13","doi-asserted-by":"crossref","unstructured":"Sepas-Moghaddam, A., Etemad, A.: Deep gait recognition: a survey. IEEE Trans. Pattern\u00a0Anal.\u00a0Mach. Intell. 45(1), 264\u2013284 (2023)","DOI":"10.1109\/TPAMI.2022.3151865"},{"key":"34_CR14","unstructured":"Shen, C., Yu, S., Wang, J., Huang, G.Q., Wang, L.: A comprehensive survey on deep gait recognition: algorithms, datasets and challenges (2022)"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl. 10, 4 (2018)","DOI":"10.1186\/s41074-018-0039-6"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Tan, D., Huang, K., Yu, S., Tan, T.: Efficient night gait recognition based on template matching. In: 18th International Conference on Pattern Recognition (ICPR\u201906). vol. 3, pp. 1000\u20131003 (2006)","DOI":"10.1109\/ICPR.2006.478"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Teepe, T., Khan, A., Gilg, J., Herzog, F., H\u00f6rmann, S., Rigoll, G.: Gaitgraph: graph convolutional network for skeleton-based gait recognition. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 2314\u20132318 (2021)","DOI":"10.1109\/ICIP42928.2021.9506717"},{"key":"34_CR18","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019). https:\/\/github.com\/facebookresearch\/detectron2"},{"key":"34_CR19","unstructured":"Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 4, pp. 441\u2013444 (2006)"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Zhu, Z., et al.: Gait recognition in the wild: A benchmark. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 14769\u201314779 (2021)","DOI":"10.1109\/ICCV48922.2021.01452"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4742-3_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T09:06:10Z","timestamp":1729847170000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4742-3_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947416","9789819947423"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4742-3_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}