{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:04:41Z","timestamp":1750309481324,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,9]]},"DOI":"10.1145\/3697467.3697629","type":"proceedings-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T21:31:52Z","timestamp":1731101512000},"page":"161-166","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Human Behavior Recognition Based on 3D-Ghostnet"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4229-9778","authenticated-orcid":false,"given":"Jiang","family":"Shi","sequence":"first","affiliation":[{"name":"CHN Energy Transportation Technology Research Institute Co., Ltd., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5653-593X","authenticated-orcid":false,"given":"Ye","family":"Hou","sequence":"additional","affiliation":[{"name":"CHN Energy Shuohuang Railway Development Co.,Ltd., Cangzhou, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3754-6114","authenticated-orcid":false,"given":"Mingfeng","family":"Guan","sequence":"additional","affiliation":[{"name":"CHN Energy Shuohuang Railway Development Co.,Ltd., Cangzhou, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5950-6416","authenticated-orcid":false,"given":"Hu","family":"Ren","sequence":"additional","affiliation":[{"name":"CHN Energy Shuohuang Railway Development Co.,Ltd., Cangzhou, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8026-4895","authenticated-orcid":false,"given":"Qixiang","family":"Li","sequence":"additional","affiliation":[{"name":"East China Jiaotong University, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1597-7735","authenticated-orcid":false,"given":"Yupeng","family":"Liu","sequence":"additional","affiliation":[{"name":"East China Jiaotong University, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_3_3_1_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-08659-2"},{"key":"e_1_3_3_1_2_2","unstructured":"Liu ZH Luo YZ. Intelligent video supervising technologies and their applications in security[J]. Ordnance Industry Automation. 2009; 4:7578."},{"key":"e_1_3_3_1_3_2","volume-title":"A model based method of pedestrian abnormal behavior detection in traffic scene[C]\/\/2015 IEEE First International Smart Cities Conference (ISC2)","author":"Qianyin J","year":"2015","unstructured":"Qianyin J, Guoming L, Jinwei Y, et al. A model based method of pedestrian abnormal behavior detection in traffic scene[C]\/\/2015 IEEE First International Smart Cities Conference (ISC2). IEEE, 2015: 1-6."},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09724-5"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Wang H Schmid C. Action recognition with improved trajectories[C]\/\/Proceedings of the IEEE international conference on computer vision. 2013: 3551-3558.","DOI":"10.1109\/ICCV.2013.441"},{"key":"e_1_3_3_1_6_2","volume-title":"Human behaviour recognition using deep learning[C]\/\/2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","author":"Lu J","year":"2018","unstructured":"Lu J, Yan W Q, Nguyen M. Human behaviour recognition using deep learning[C]\/\/2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2018: 1-6."},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Tran D Bourdev L Fergus R et al. Learning spatiotemporal features with 3d convolutional networks[C]\/\/Proceedings of the IEEE international conference on computer vision. 2015: 4489-4497.","DOI":"10.1109\/ICCV.2015.510"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Carreira J Zisserman A. Quo vadis action recognition? a new model and the kinetics dataset[C]\/\/proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 6299-6308.","DOI":"10.1109\/CVPR.2017.502"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Qiu Z Yao T Mei T. Learning spatio-temporal representation with pseudo-3d residual networks[C]\/\/proceedings of the IEEE International Conference on Computer Vision. 2017: 5533-5541.","DOI":"10.1109\/ICCV.2017.590"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Tran D Wang H Torresani L et al. A closer look at spatiotemporal convolutions for action recognition[C]\/\/Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 2018: 6450-6459.","DOI":"10.1109\/CVPR.2018.00675"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Han K Wang Y Tian Q et al. Ghostnet: More features from cheap operations[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020: 1580-1589.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Wang X Girshick R Gupta A et al. Non-local neural networks[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7794-7803.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"e_1_3_3_1_13_2","volume-title":"Is space-time attention all you need for video understanding? [C]\/\/ICML","author":"Bertasius G","year":"2021","unstructured":"Bertasius G, Wang H, Torresani L. Is space-time attention all you need for video understanding? [C]\/\/ICML. 2021, 2(3): 4."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Xie C Wu Y Maaten L et al. Feature denoising for improving adversarial robustness[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2019: 501-509.","DOI":"10.1109\/CVPR.2019.00059"},{"key":"e_1_3_3_1_15_2","volume-title":"Convolutional neural networks with low-rank regularization[J]. arXiv preprint arXiv:1511.06067","author":"Tai C","year":"2015","unstructured":"Tai C, Xiao T, Zhang Y, et al. Convolutional neural networks with low-rank regularization[J]. arXiv preprint arXiv:1511.06067, 2015."},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Huang Z Wang X Huang L et al. Ccnet: Criss-cross attention for semantic segmentation[C]\/\/Proceedings of the IEEE\/CVF international conference on computer vision. 2019: 603-612.","DOI":"10.1109\/ICCV.2019.00069"},{"issue":"9","key":"e_1_3_3_1_17_2","first-page":"4839","article-title":"Depthwise spatio-temporal STFT convolutional neural networks for human action recognition [J]","volume":"44","author":"Kumawat S","year":"2021","unstructured":"Kumawat S, Verma M, Nakashima Y, et al. Depthwise spatio-temporal STFT convolutional neural networks for human action recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(9): 4839-4851.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_3_1_18_2","volume-title":"Liu Y. Actionclip: A new paradigm for video action recognition [J]. arXiv preprint arXiv: 2109.08472","author":"Wang M","year":"2021","unstructured":"Wang M, Xing J, Liu Y. Actionclip: A new paradigm for video action recognition [J]. arXiv preprint arXiv: 2109.08472, 2021."},{"key":"e_1_3_3_1_19_2","volume-title":"Temporal 3d convnets: New architecture and transfer learning for video classification [J]. arXiv preprint arXiv:1711.08200","author":"Diba A","year":"2017","unstructured":"Diba A, Fayyaz M, Sharma V, et al. Temporal 3d convnets: New architecture and transfer learning for video classification [J]. arXiv preprint arXiv:1711.08200, 2017."},{"key":"e_1_3_3_1_20_2","volume-title":"Temporal segment networks: Towards good practices for deep action recognition[C]\/\/European conference on computer vision","author":"Wang L","year":"2016","unstructured":"Wang L, Xiong Y, Wang Z, et al. Temporal segment networks: Towards good practices for deep action recognition[C]\/\/European conference on computer vision. Springer, Cham, 2016: 20-36."}],"event":{"name":"IoTML 2024: 2024 4th International Conference on Internet of Things and Machine Learning","acronym":"IoTML 2024","location":"Nanchang China"},"container-title":["Proceedings of the 2024 4th  International Conference on Internet of Things and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3697467.3697629","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3697467.3697629","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:30Z","timestamp":1750295850000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3697467.3697629"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,9]]},"references-count":20,"alternative-id":["10.1145\/3697467.3697629","10.1145\/3697467"],"URL":"https:\/\/doi.org\/10.1145\/3697467.3697629","relation":{},"subject":[],"published":{"date-parts":[[2024,8,9]]},"assertion":[{"value":"2024-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}