{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:39:33Z","timestamp":1743118773497,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819770007"},{"type":"electronic","value":"9789819770014"}],"license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"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":[[2025]]},"DOI":"10.1007\/978-981-97-7001-4_31","type":"book-chapter","created":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T18:01:43Z","timestamp":1726941703000},"page":"437-448","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pedestrian Fall Detection Algorithm Based on Improved YOLOv7"],"prefix":"10.1007","author":[{"given":"Fei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunchu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,22]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Li, X., Pang, T., Liu, W., et al.: Fall detection for elderly person care using convolutional neural networks. In: 10th International Congress on Image and Signal Processing, pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/CISP-BMEI.2017.8302004"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Li, J., Zhao, Q., Yang, T., et al.: An algorithm of fall detection based on vision. In: 6th International Symposium on Computer and Information Processing Technology, pp.133\u2013136. IEEE (2021)","DOI":"10.1109\/ISCIPT53667.2021.00033"},{"issue":"4","key":"31_CR3","first-page":"546","volume":"27","author":"GK Hader","year":"2020","unstructured":"Hader, G.K., Ben Ismail, M., et al.: Automatic fall detection using region-based convolutional neural network. J. Int. Syst. 27(4), 546\u2013557 (2020)","journal-title":"J. Int. Syst."},{"key":"31_CR4","unstructured":"Yang, X., Xu, T., Guo, Z., et al.: A human fall detection method based on YOLO network. J. Jou. 29(2), 61\u201364 (2019)"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Cai, X., Liu, X., An, M., et al.: Vision-based fall detection using dense block with multi-channel convolutional fusion strategy. J. IEEE Access 18318\u201318325 (2021)","DOI":"10.1109\/ACCESS.2021.3054469"},{"key":"31_CR6","unstructured":"Shen, G., Wei, Y., et al.: An improved YOLOv5 algorithm for pedestrian fall detection. J. Small Microcomput. Syst. 1\u20139 (2023)"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Zhao, D., Song, T., Gao, J., et al.: YOLO-fall: a novel convolutional neural network model for fall detection in open spaces. J. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3362958"},{"key":"31_CR8","doi-asserted-by":"publisher","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision \u2013 ECCV 2016. ECCV 2016. Lecture Notes in Computer Science(), vol. 9905, pp. 21\u201337 . Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"31_CR9","unstructured":"Park, J., Woo, S., Lee, J.Y., et al.: Bam: bottleneck attention module. In: Proceedings of the 2018 British Machine Vision (ECCV), pp. 147 (2018)"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation, pp.580\u2212587. IEEE (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Zhao, H.S., Shi, J.P., Qi, X.J., et al.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6230\u20136239 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., Liao, H.Y.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464\u22127475 (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13713\u221213722 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Yu, W., Pan, Z., Shui, Y., et al.: Inceptionnext: When inception meets convnext (2023)","DOI":"10.1109\/CVPR52733.2024.00542"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., et al.: A ConvNet for the 2020s. In: Conference on Computer Vision and Pattern Recognition, pp .11966\u201311976 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Yan, L., Bern, B, Denker, John, S., et al.: Backpropagation applied to handwrit-ten zip code recognition. Neural Comput. 541\u2013551 (1989)","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"31_CR18","unstructured":"Yong, R., Wen, Z., Yan, T., et al.: Hornet: Efficient high-order spatial interactions with recursive gated convolutions (2022)"},{"key":"31_CR19","unstructured":"Tong, Z., Chen, Y., Xu, Z., et al.: Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism (2023)"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Zheng, Z.H., Wang, P., Liu, W., et al.: Distance-IoU loss: faster and better learning for bounding box regression. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 12993\u201313000. AAAI, New York (2020)","DOI":"10.1609\/aaai.v34i07.6999"}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-7001-4_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T18:03:42Z","timestamp":1726941822000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-7001-4_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,22]]},"ISBN":["9789819770007","9789819770014"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-7001-4_31","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024,9,22]]},"assertion":[{"value":"22 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aaci.org.hk\/ncaa2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}