{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:59:44Z","timestamp":1742921984274,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819714162"},{"type":"electronic","value":"9789819714179"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-1417-9_13","type":"book-chapter","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T07:05:03Z","timestamp":1716275103000},"page":"133-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GaitMG: A Multi-grained Feature Aggregate Network for\u00a0Gait Recognition"],"prefix":"10.1007","author":[{"given":"Jiwei","family":"Wan","sequence":"first","affiliation":[]},{"given":"Huimin","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Rongjun","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Tuanjie","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Yongqi","family":"Ren","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,22]]},"reference":[{"key":"13_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. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 8126\u20138133 (2019)","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Chen, R., et al.: Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed MobileNet. IEEE Internet Things J. (2023)","DOI":"10.1109\/JIOT.2023.3268636"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Fan, C., et al.: GaitPart: temporal part-based model for gait recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14225\u201314233 (2020)","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"13_CR4","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint: arXiv:1703.07737 (2017)"},{"issue":"6","key":"13_CR5","first-page":"3742","volume":"62","author":"C Hong","year":"2014","unstructured":"Hong, C., Yu, J., Tao, D., Wang, M.: Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans. Industr. Electron. 62(6), 3742\u20133751 (2014)","journal-title":"IEEE Trans. Industr. Electron."},{"key":"13_CR6","series-title":"Lecture Notes in Computer Science()","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1007\/978-3-030-58545-7_22","volume-title":"Computer Vision - ECCV 2020","author":"S Hou","year":"2020","unstructured":"Hou, S., Cao, C., Liu, X., Huang, Y.: Gait lateral network: learning discriminative and compact representations for gait recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. Lecture Notes in Computer Science(), vol. 12354, pp. 382\u2013398. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_22"},{"issue":"6","key":"13_CR7","doi-asserted-by":"publisher","first-page":"6496","DOI":"10.1007\/s10489-022-03693-z","volume":"53","author":"Z Hou","year":"2023","unstructured":"Hou, Z., Li, F., Wang, S., Dai, N., Ma, S., Fan, J.: Video object segmentation based on temporal frame context information fusion and feature enhancement. Appl. Intell. 53(6), 6496\u20136510 (2023)","journal-title":"Appl. Intell."},{"issue":"12","key":"13_CR8","doi-asserted-by":"publisher","first-page":"2034","DOI":"10.1109\/TIFS.2013.2287605","volume":"8","author":"M Hu","year":"2013","unstructured":"Hu, M., Wang, Y., Zhang, Z., Little, J.J., Huang, D.: View-invariant discriminative projection for multi-view gait-based human identification. IEEE Trans. Inf. Forensics Secur. 8(12), 2034\u20132045 (2013)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Huang, X., et al.: Context-sensitive temporal feature learning for gait recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12909\u201312918 (2021)","DOI":"10.1109\/ICCV48922.2021.01267"},{"key":"13_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint: arXiv:1412.6980 (2014)"},{"key":"13_CR11","doi-asserted-by":"publisher","first-page":"108453","DOI":"10.1016\/j.patcog.2021.108453","volume":"124","author":"H Li","year":"2022","unstructured":"Li, H., et al.: GaitSlice: a gait recognition model based on spatio-temporal slice features. Pattern Recogn. 124, 108453 (2022)","journal-title":"Pattern Recogn."},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Li, X., Makihara, Y., Xu, C., Yagi, Y., Yu, S., Ren, M.: End-to-end model-based gait recognition. In: Proceedings of the Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69535-4_1"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing. IEEE Trans. Geosci. Remote Sens. (2023)","DOI":"10.1109\/TGRS.2023.3276589"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Lin, B., Zhang, S., Bao, F.: Gait recognition with multiple-temporal-scale 3D convolutional neural network. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3054\u20133062 (2020)","DOI":"10.1145\/3394171.3413861"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Lin, B., Zhang, S., Yu, X.: Gait recognition via effective global-local feature representation and local temporal aggregation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14648\u201314656 (2021)","DOI":"10.1109\/ICCV48922.2021.01438"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"108520","DOI":"10.1016\/j.patcog.2022.108520","volume":"125","author":"X Liu","year":"2022","unstructured":"Liu, X., You, Z., He, Y., Bi, S., Wang, J.: Symmetry-driven hyper feature GCN for skeleton-based gait recognition. Pattern Recogn. 125, 108520 (2022)","journal-title":"Pattern Recogn."},{"key":"13_CR17","first-page":"1","volume":"61","author":"P Ma","year":"2023","unstructured":"Ma, P., et al.: Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 61, 1\u201312 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"5","key":"13_CR18","doi-asserted-by":"publisher","first-page":"2990","DOI":"10.1109\/TCSVT.2021.3095290","volume":"32","author":"H Qin","year":"2021","unstructured":"Qin, H., Chen, Z., Guo, Q., Wu, Q.J., Lu, M.: RPNet: gait recognition with relationships between each body-parts. IEEE Trans. Circuits Syst. Video Technol. 32(5), 2990\u20133000 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"13_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s41074-017-0037-0","volume":"10","author":"N Takemura","year":"2018","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, 1\u201314 (2018)","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Wolf, T., Babaee, M., Rigoll, G.: Multi-view gait recognition using 3D convolutional neural networks. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 4165\u20134169. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7533144"},{"key":"13_CR21","doi-asserted-by":"publisher","first-page":"2734","DOI":"10.1109\/TIP.2020.3039888","volume":"30","author":"H Wu","year":"2020","unstructured":"Wu, H., Tian, J., Fu, Y., Li, B., Li, X.: Condition-aware comparison scheme for gait recognition. IEEE Trans. Image Process. 30, 2734\u20132744 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"13_CR22","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\u201906), vol.\u00a04, pp. 441\u2013444. IEEE (2006)"},{"key":"13_CR23","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1007\/s12559-013-9221-4","volume":"6","author":"W Zeng","year":"2014","unstructured":"Zeng, W., Wang, C., Li, Y.: Model-based human gait recognition via deterministic learning. Cogn. Comput. 6, 218\u2013229 (2014)","journal-title":"Cogn. Comput."},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Gait recognition via disentangled representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4710\u20134719 (2019)","DOI":"10.1109\/CVPR.2019.00484"},{"issue":"8","key":"13_CR25","doi-asserted-by":"publisher","first-page":"4032","DOI":"10.1109\/JBHI.2022.3177854","volume":"26","author":"H Zhao","year":"2022","unstructured":"Zhao, H., et al.: SC2Net: a novel segmentation-based classification network for detection of COVID-19 in chest x-ray images. IEEE J. Biomed. Health Inform. 26(8), 4032\u20134043 (2022)","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Lecture Notes in Computer Science","Advances in Brain Inspired Cognitive Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-1417-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T14:30:26Z","timestamp":1732026626000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-1417-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819714162","9789819714179"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-1417-9_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The data that supports the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. The authors declare that they have no conflict of interest. This article does not contain any studies with human participants performed by any of the authors.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data Availability Statement and Compliance with Ethical Standards"}},{"value":"BICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Inspired Cognitive Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kuala Lumpur","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaysia","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":"5 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bics2023","order":10,"name":"conference_id","label":"Conference ID","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","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":"36","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":"62% - 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":"5","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)"}}]}}